math phd while working

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What to do after a pure math academic path?

I don't know whether my question is in the appropriate place. I've studied physics, and then did a PhD in (pure) math and 2 postdocs. I definitely love math research, but I am not ready to apply all over the world hoping to find a position somewhere sometimes. Therefore I am looking for a job.

I don't have any interest in anything from the society. I only love math for its beauty. I am wondering what happened to the world. All jobs I am looking for with "math diploma" requirement seems to be in data science or finance. I hate this stuff and don't see the relation with math, at least the math that I like. I cannot see any beauty in data science and worse, in finance.

Does anyone have an idea of not-so-sad job openings? Is it our fate to change our career paths to finance if we had a pure math-physics academic background? Sorry for these desperate questions, but I feel so lost and sad….

• soft-question
• 19 $\begingroup$ I'm not sure if anything has "happened to the world" - 30 years ago data science didn't exist, and your only choice was finance. Also, data science and finance are both huge fields, and I have to wonder how hard you've actually looked for beauty (mathematical or otherwise) in them. How much do you know about the theory of stochastic differential equations? Information theory? Bayesian inference? $\endgroup$ –  Paul Siegel Commented Oct 21, 2022 at 17:50
• 23 $\begingroup$ There's always secondary school teaching, but that can be hard if you're not called to it. But I would take a broader view of the high-tech industry. I have former PhD students working at places like Google, and they seem to enjoy the day-to-day challenges of their job, and seem mostly fairly happy. There's also a broader message I would give you that I think is important for people thinking about leaving academia. In the culture of academia, there is a strong emphasis of intensely loving mathematics itself, and (continued) $\endgroup$ –  Andy Putman Commented Oct 21, 2022 at 17:58
• 37 $\begingroup$ tying your sense of meaning and satisfaction very directly to your work. I think I'm lucky in that neither of my parents were academics, so I grew up with a better sense of how normal people relate to their job. Namely, they both liked their jobs, but less because of what they did and more because they liked their coworkers a lot and found their work mostly not too boring. Neither of them would say that their job gave their life meaning (that came from their family, community, hobbies, etc.). I think that's a healthier way to live, to be honest. $\endgroup$ –  Andy Putman Commented Oct 21, 2022 at 17:58
• 32 $\begingroup$ As someone in a similar boat, I've chosen to pursue a career path that gives me plenty of time to dedicate to mathematics research -- I don't know what country you're in, but in America being a firefighter is a fantastic option. Good pay, you get to sit at the firehouse working on whatever if there are no fires, and you usually have 3-4 days a week off. (Also you can pursue a degree in fire science all the way up to a PhD if you like, and this improves your career prospects for being a fire chief etc.) $\endgroup$ –  Alec Rhea Commented Oct 21, 2022 at 18:01
• 15 $\begingroup$ @coco: I can only imagine the terrible advice you would get here re that! I would focus on choosing a place where you would like to live, and then get deeply involved in your local community. Fundamentally, other people (not necessarily family!) are a more stable source of happiness and meaning than a job. $\endgroup$ –  Andy Putman Commented Oct 21, 2022 at 18:15

8 Answers 8

I am sorry that the OP feels "desperate and sad." I agree with the comments suggesting that happiness in life is very different from achieving some specific career. I also think a lot has to do with mindset.

That said, there are zillions of jobs for mathematicians (far from data science and finance being the only options) and many of them involve working with beautiful mathematical concepts. Here are some examples, in no particular order:

• Use math to identify cases of gerrymandering and help create maps that are fair. This involves graph theory, geometry, metric spaces, and more. It's very cool and super relevant.
• Become a senior scientist or research mathematician at a tech company, like the sort that hired Jennifer Chayes, Laszlo Lovasz, Katalin Vesztergombi, etc. There is plenty of beautiful work to do in graph theory.
• Social network analysis is a lovely blend of mathematics and sociology. I saw a great talk by Strogatz on this topic once. I imagine companies like Meta might have teams of mathematicians studying social network graphs.
• Topological data analysis (TDA) is beautiful to a lot of people, and involves mathematical concepts such as graphs, metric spaces, Betti numbers, and a whole lot more. There are government and industry research groups based on TDA, and it's a growing area. Lots of jobs.
• Work for a government intelligence service. Plenty of connections to graph theory, number theory, etc. If you like your government and believe its mission is protecting people, then this kind of work can be immensely rewarding.
• Work for a government contractor, like the IDA in the USA. I know people in jobs like that who spend most of their time thinking about elliptic curves, group laws, error correcting codes, etc.
• Be an actuary. If you like probability theory and probability models, there are really fun topics that come up in this setting.
• I push back against the idea that there is no beauty in data science. Many data mining algorithms involve beautiful mathematics, like principal component analysis (eigenvectors, change of basis), singular value decomposition and separating hyperplanes, graph clustering algorithms, etc. Many companies have realized that if they want to get their modeling right, it's beneficial to have a trained mathematician onboard rather than only people who know how to run commands and have no idea why the algorithm works. I know data scientists who spend their time tweaking these algorithms to work in new settings, which means they are constantly playing with these beautiful concepts. Additionally, there is tremendous satisfaction in feeling like you created something that has the ability to really help a large number of people in their lives, e.g., statistical models to inform government policy and help lift people out of poverty, match people to jobs they will enjoy, help people who use drugs to get out of a state of addiction, etc.
• I know a lot of people who think Fourier analysis is beautiful and there's a whole branch of data science (spectral theory, time series models) where you get to play with this every day. Same for working for companies like Sound Hound or Shazam, and probably many others that I haven't listed (Zoom? Skype? How do they denoise? Some beautiful math must be in the background.)
• I concur with comments who said secondary school teaching can be a very fulfilling job, and one full of opportunities to enjoy (and share) the beauty of math. That's especially true if you work with the IMO team, programs for gifted high school students, etc. Such students can even do cool research and there have been lots of MO questions about that topic.
• I believe certain types of engineering use fairly sophisticated tools from analysis. Sadly, I'm not an expert in this.
• Text analysis, e.g., using and developing algorithms for determining authorship, extracting summaries, etc. Imagine developing an algorithm that can use Twitter data to figure out when an emergency is happening and then dynamically allocate government resources to help.
• Mathematical art, both creating it and using math to connect people with art in new ways (e.g., Google Deep Dream)
• Using math to create improved epidemiological models, e.g., while working for a hospital system, government, etc.

Others have compiled better lists than this, e.g., the AMS has a list including the following and also a list of other lists.

• Climate study
• Animated films
• Astronomy and space exploration

I guess the message I want to impart to the OP is that there's a lot to be excited about and a lot to look forward to. Now that you're a trained mathematician, you can go in many directions. For almost any passion, there is a way to connect it to mathematics and to bring the beauty of math into that world. Go explore and play!

• 14 $\begingroup$ "Imagine developing an algorithm that can use Twitter data to figure out when an emergency is happening and then dynamically allocate government resources to help." This example made me smile, because my team has worked on this sort of problem before, but our contacts at Twitter get very anxious when you talk about using their data to collaborate with governments. $\endgroup$ –  Paul Siegel Commented Oct 21, 2022 at 19:09
• 4 $\begingroup$ Wonderful response. I should add that a lot of mathematicians (especially young people) turn towards research in artificial intelligence and machine learning. $\endgroup$ –  GH from MO Commented Oct 21, 2022 at 19:44
• 3 $\begingroup$ Mathematics can turn up in some unexpected places - see Ian Stewart's What's the Use? , for instance. I was also surprised recently to happen across Tom Leinster's book: Entropy and Diversity: The Axiomatic Approach and to see some (to me) unexpected areas of mathematics applied to biodiversity. $\endgroup$ –  J W Commented Oct 22, 2022 at 12:07
• 4 $\begingroup$ Some of these wouldn't be jobs themselves, but a contract done by a consulting firm, for example. Anything overly specific might require OP to move. A lot require willingness to code. $\endgroup$ –  justforplaylists Commented Oct 22, 2022 at 13:31
• 9 $\begingroup$ ...Unfortunately, all these avenues have a premise that you actually do not suck at math, and do not really have to choose non-academic job. (...also the number of times collaboration with governmental/big corp global surveillance teams is mentioned makes me uncomfortable) $\endgroup$ –  Denis T Commented Oct 22, 2022 at 15:21

I love mathematics, too, but I don’t expect to get paid unless I do mathematics that makes money for my employer. Nothing has “happened to the world” — it has always been this way.

If you love mathematics purely for its beauty, and you don’t care whether it provides any value to society (or your employer), then perhaps you should think of yourself as an artist, like a painter, sculptor, or musician.

To make a living as an artist, you need an audience, and the audience for modern pure mathematics is extremely small. As a musician, you can go play on street corners, and maybe make enough money to live, but it’s a hard life. The modern equivalent of the street corner is a YouTube channel. You could try that, but most potential subscribers are looking for help with calculus, and wouldn’t be interested in your research work.

Another alternative is to look for a job as an academic mathematician. As you said, this will probably involve hunting around the world for a while, and you might have to go live somewhere that’s not as pleasant as Switzerland. You said you don’t want to do this. Fair enough. Your choice.

A third alternative is to take a job that provides you with enough cash to live, and yet still allows you enough free time to pursue your art. Then you don’t need to worry about finding an audience, and you can just do things that you personally find beautiful, regardless of what anyone else thinks of them — you’re free. You said you couldn’t be a firefighter, but there are other jobs that consist mostly of sitting and waiting. Many of these jobs don’t pay very well, but I’m guessing that this might not bother you.

If none of the above sounds appealing, then maybe it’s time to re-evaluate. Do you have to do mathematics research? Could you live without it? Is it as important to you as your friends, family, mental and physical health? Could you find the same beauty in some other discipline?

The way out for me (and many others) was through software development. Learning programming is easy enough, and it’s a highly marketable skill. You won’t use much of the mathematics you learned, but well-constructed software has much of the same elegance and beauty as mathematics (in my opinion). Something to consider.

• 13 $\begingroup$ Good answer. I am confused by the OP's comment, "I don't have any interest in anything from the society." But a job is something from the society and the OP is interested in a job. I am sympathetic to the OP's plight, but the starting point has to be a recognition that if you want something (a job) from society then you have to offer something in return. The OP does not like the concept of money , but a job is something that an employer gives you money for doing. $\endgroup$ –  Timothy Chow Commented Oct 23, 2022 at 10:20
• 3 $\begingroup$ +1 last paragraph – software development definitely benefits from rigorous/logical thinking and good sense, though some practitioners are loath to admit it. $\endgroup$ –  Zhen Lin Commented Oct 23, 2022 at 23:12
• 2 $\begingroup$ The problem with software is that your employer is most likely to want you to create poorly-constructed software cheaply and quickly rather than create well-constructed software. $\endgroup$ –  Alexander Woo Commented Oct 24, 2022 at 22:54
• 2 $\begingroup$ @AlexanderWoo I don’t know if I’d agree with “most likely”, but some employers certainly think that way. Writing shoddy software cheaply and quickly might maximize short-term profits, but damage long-term ones. And without short-term profits, there is no long term, in some companies. So, in those companies, cheap and fast are the right objectives, and everything else is a luxury. $\endgroup$ –  bubba Commented Oct 26, 2022 at 8:15
• 1 $\begingroup$ Well, you can go somewhere and live as a self-sufficient farmer, if that’s how you want to provide yourself with food, clothing, and shelter. But that’s a taxing full-time job, and you might not have enough time/energy left over to do mathematics. At the other end of the spectrum, you might be able to make enough money to live by working 20 hours/week as a contract programmer. $\endgroup$ –  bubba Commented Oct 29, 2022 at 2:19

I sympathize with you, because I have been in a similar situation. I was in mathematics because it fascinated me, although sometimes more than others. The reason I left mathematics research was not so much that I got tired from all the moving around, but that I wanted to do something "in the real world." I felt that my kind of research was hard to justify to anyone but a specialist, and that mattered to me.

But when I started to make an inventory of options that were available to me on the regular job market, I found out, exactly like you, that the vast majority of jobs for which a mathematics degree is a requirement (or even a plus) struck me as particularly "soulless." I know that might strike some as harsh, but I don't mean to be, that's simply how it felt at the time. I was in mathematics for the joy of it, and it is hard to square this with the purely utilitarian approach to math you find in finance or data science.

In the end I got a job in software development, which at first seemed just as soulless or maybe even more. However I never regretted my decision to leave mathematics. The joy that mathematics had to offer that I just mentioned was of a very elusive kind: sometimes it was there in abundance, but I could never hold on to it. It wasn't a constant stream of inspiration, and what's worse I rarely experienced it during my own "research" (if I could call it that), but almost always by reading about the exciting work done by others. And it didn't have to be cutting edge either.

So yes, quitting the academic career path was a major adjustment, and a period where I experienced loss. I was no longer allowed to devote my life to the pursuit of knowledge and understanding. Worse, I started to question whether I hadn't in fact thrown away fifteen years of my life. But there is light on the other end of the tunnel. These are after all not math problems, but life problems. If you make a decision that you know is right, then, with God's grace, it will prove to be so in the end.

• 4 $\begingroup$ Within the big area of SW-development I'd point out that for people strong in mathematics implementing/developing cryptographic algorithms (likely protected against side-channel attacks) is a fun topic where learning to program the right programming language(s) and the necessary cryptographic background shouldn't be hard for a math PhD. $\endgroup$ –  j.p. Commented Oct 22, 2022 at 7:31
• 2 $\begingroup$ It's also a really really hard problem. Computers have side-channels that the manufacturers insist don't exist. Worthwhile, too, if you think cryptography's better to have than not. $\endgroup$ –  wizzwizz4 Commented Oct 22, 2022 at 14:17
• 8 $\begingroup$ +1 software developer (I left academia after my algebra PhD). I recommend getting into functional programming if you can - I'm a Scala dev now - since this uses category theory and is generally more abstract and mathematical in a way that works well for someone with a maths background. But programming in general scratches the same problem-solving-using-logical-deduction itch that I loved about doing maths. It's just that the problems are much smaller and more tractable, which on the one hand means the highs don't compare but on the other means you don't get stuck nearly as long (IME). $\endgroup$ –  Astrid Commented Oct 23, 2022 at 9:30
• 6 $\begingroup$ Also, on the soulless front - you can find jobs where you're doing meaningful work in tech, it just takes some effort, may mean you need to make sacrifices in other areas (chosen programming language, amount of tech debt, location, salary, etc.), and it's possible you can only be that selective once you have experience under your belt. But among others I've worked on software for healthcare and public transport and very much felt we were creating something meaningful that would help people. $\endgroup$ –  Astrid Commented Oct 23, 2022 at 9:41
• 1 $\begingroup$ @coco I could probably more accurately describe myself as a web developer. I develop .NET web applications for a decent-sized company, think online shopping, B2B apps, and the CRM app for internal use. It has absolutely zero to do with mathematics. But there is a lot of creative problem solving involved, and the more experience you have, the more you get to see that. $\endgroup$ –  R.P. Commented Oct 27, 2022 at 13:24

There are two types of career paths for a pure mathematics PhD holder who wants to continue to pursue pure mathematics:

Mathematics-related jobs, typically in academia: Most people only look at university jobs which involve teaching and research in roughly balanced proportions. However, one should not ignore school and college teaching positions. These can be extremely rewarding and can lead to interesting research questions in pure mathematics.

Jobs entirely unrelated to mathematics: Here the opportunities are limited only by the current job market in your location. Note that a PhD is a higher qualification than what most people in the job market have. (In some places this could unfortunately be a dis-qualification.) It would then be incumbent on you to pursue mathematics on your own; which may be possible provided your job does not suck up your time and mind-space.

Unfortunately, many people are caught in the trap of looking for a job that "is commensurate with their qualifications" where the measurement is based external factors like remuneration and respect from society at large. Such jobs may require you to give up pure mathematics. The alternative is to look for a job which is sufficient to support the goal of pursuing one's primary interests.

Here is a tale that may be inspirational. The person who taught me music was a skilled worker in a workshop (factory-type). Even though it did not pay very well, it provided adequate support as far as he was concerned. This allowed him to develop and enjoy his music over a 30-40 year period. He never became famous, but he enjoyed and developed his music, and spread this joy to everyone he taught or otherwise came in contact with.

• 3 $\begingroup$ +1 teaching math at a 4-year or 2-year college. (Many of these even require Ph.D.) Look into this if non-academic positions seem pointless to you. $\endgroup$ –  Gerald Edgar Commented Oct 22, 2022 at 8:35
• $\begingroup$ @GeraldEdgar the problem with teaching in high school is that one needs to deal with discipline problems rather than teaching maths.. also one needs to study another 2 years to have the right to teach. $\endgroup$ –  coco Commented Oct 26, 2022 at 14:43
• 1 $\begingroup$ @kapil thanks for the story of the musician :) $\endgroup$ –  coco Commented Oct 28, 2022 at 17:37

I got a PhD in math and was on this path myself, so I understand. The number one thing you have to realize is that math for the sake of its beauty is hard to pursue even in research academia sometimes. Therefore, keep that part separated in your mind.

The next thing you should do is re-evaluate exactly what you want to do in life, regardless of whether it involved math or not. In other words, keep a blank slate. You basically have to do this because as I said, outside of academia you will never find something that will suit your ideals and in fact it's often hard inside academia. (For example, I love the beauty of math but find the mainstream of endless specialization in huge overarching fields not my thing.)

Once you decide what will really make you happy, just go for it even if it's not math-related. Why? Because you will be much happier doing math on the side than you EVER will be doing a math-related job that doesn't appeal to your ideals. Personally, it only took me a couple years of doing math that didn't appeal to me to make me lose a lot of my passion for it.

My only other advice is get a high-paying job like software development or something applied for 4-5 years. Be frugal and save up a ton of money and then just use it to pursue your passions. Make a plan to exit the traditional system and just do it and don't look back. Math does not define you as a person and I am sure that once you find your center, you will understand what you need to do.

• 1 $\begingroup$ Many thanks for your answer! Indeed that's my problem, i do not know what i want to do in life. I know that life in academia is also not paradise, but at least there is a part of it that we like. Though it is true that i don't like pressure and i like to have enough time to think about something, which in general is until i find the solution. But that solution can come in a too big amount of time, and one needs to publish regularly enough, so one should work on a project that will work for sure. $\endgroup$ –  coco Commented Oct 28, 2022 at 17:55
• 1 $\begingroup$ So you are right that doing math as a hobby also has an advantage if we have time to. I have seen your photos, they are absolutely gorgeous!! :) $\endgroup$ –  coco Commented Oct 28, 2022 at 17:58
• 1 $\begingroup$ @coco - Thank you :) I agree, a job in academia is relatively good and should allow you to explore your interests part of the time. I hope you find your path. It's not always easy to do so. $\endgroup$ –  user1437 Commented Oct 30, 2022 at 3:57

Some of the recently emerged fields in machine learning have a bit of overlap with mathematics (not sure how pure they are). I'm going to name a few that comes to mind:

• Graph theory : a recently introduced network architecture known as graph neural network can be considered a generalization of belief propagation networks on structured graph. This may in addition have some overlap with statistical physics.
• Algebra/geometry : equivariant neural networks require some sort of explicit/implicit symmetry built into each layer, and very recently people have studied this type of network with symmetry related to certain Lie algebras
• Topology/measure theory : to give a marginally related example, normalization flows are neural networks that attempts to "continuously" deform a Gaussian measure to some other non-trivial (usually multi-modal) measures. For instance, people have used the Banach fixed point theorem to show such networks are actually "trainable".
• Complexity theory : transformers are a type of network that requires quadratic memory and compute to perform inference. Recently, people have investigated ways to reduce its complexity theory methods such as hashing and kernel methods .
• Optimization theory : currently the way neural networks are trained are somewhat ad hoc, and people just use whatever optimizers (e.g. Adam, SAM) that gives the best empirical results. Recently, people have started looking into this more seriously, and neural ODE is a type of network that can be trained via the Pontryagin method .
• Random matrix theory : the neural network layer weights can be considered as a random matrix , and the heavy-tailness of such matrices have recently be studied as indicators for the "complexity" of the network (and whether it is prone to overfitting).
• Dynamical systems : a group in UWashington are looking at ways to interface machine learning with dynamical systems. For instance, one direction is to use neural networks to discover implicit low-rank structures of nonlinear dynamical systems, such as SINDY for the Navier Stokes.
• Fourier analysis : there is a line of research that tries to convert convolution networks into recurrent networks , by apply Fourier transforms (or polynomial decomposition) to the network inputs and kernels. Many theoretical problems are still open, such as the stability and convergence of such conversion.

However, similar to the case of physics lagging behind mathematics for 50 years or so, most ML fields further lags behind physics 20-30 years. So I wouldn't count on using a ML-related job (research or industry focused) as a medium to gain immediate access to novel mathematical research. Rather, I'd view it as an opportunity to apply your own mathematical knowledge (instead of advancing it).

• 1 $\begingroup$ Many thanks for your detailed answer and for providing interesting links! I just wonder how to find such a job, i have never seen job advertisements in these fields. $\endgroup$ –  coco Commented Oct 28, 2022 at 18:15
• 1 $\begingroup$ @coco I can only speak from my limited experience unfortunately. I initially just went on linkedin and mass applied for a bunch of ML-related jobs (not limited to research), and I just happened land a research-role at a startup that was a good fit. I'd imagine the process to be different for bigger companies, e.g. Google Brain, FAIR, etc. $\endgroup$ –  PeaBrane Commented Oct 28, 2022 at 19:00

Writing a good mathematical proof is similar to writing good code. Pure math has much more in common with software engineering than data science.

• 5 $\begingroup$ The problem is that coming up with a mathematical proof is not at all like software engineering, and writing the proof is not nearly so enjoyable for a lot of people. It's kind of like suggesting that an aspiring poet become a copyeditor instead. $\endgroup$ –  Elizabeth Henning Commented Oct 27, 2022 at 0:58

I have read your question and I am somewhat alarmed and saddened by your current state of being. I feel you need to take a broader perspective in this stage of life, zoom out; Do you really mean your only interest is in math? You also studied physics. You say "I don't have any interest in anything from the society"; are you sure?? You could contribute to medicine; how the brain might work. I'm not going to sum things up what you might do, but it vastly more than just finance or data-science. It is your quest now what else apart from pure math, you find interesting and go from there..

• 20 $\begingroup$ Not to sound too grumpy, but are you seriously telling someone who's got a PhD in pure maths and has done two postdocs to retool to contribute to medicine after they've said that line of work doesn't interest them? Your answer is not all that helpful, if it is putting the onus back on the OP ("it is your quest") and explicitly avoiding answering the question ("I'm not going to sum up..."). $\endgroup$ –  David Roberts ♦ Commented Oct 23, 2022 at 10:32
• 3 $\begingroup$ when i say i don't have interest in the society i mean i don't have a deep enough interest to devote myself to it full time. In fact between my PhD and 1st postdoc i have worked for hospital and learnt a bit of bioinformatics. I didn't like it at all and was very depressed to spend all my working time doing something i don't like. So i would like to find a happier option. $\endgroup$ –  coco Commented Oct 28, 2022 at 18:38

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Is it possible to earn a PhD while working? The brutal truth

Working alongside your PhD seems like an attractive proposal until you look at all of the different commitments you need to make to actually get a PhD and submit your dissertation. Working part-time may help PhD students financially but it often comes at an academic cost.

It is possible to earn a PhD while working. However, it requires strict time management and can be very complicated. You have to balance any other significant commitments inside and outside of your PhD.

A PhD is typically the equivalent time commitment as a full-time job. The majority of the PhD students I know work at least 40 hours a week. So, trying to get a PhD while working is very time intensive – 80-hour + weeks.

Some students drop down to a part-time PhD in order to balance all of the particular commitments of a PhD program and working hours.

Whether or not you are a part-time PhD student or you are studying your PhD full-time, here are all of the aspects you should consider if you are considering working alongside your PhD research. This is what you need to know if you are considering getting your doctorate while working.

Can you work during a PhD?

Some institutions full-out ban their PhD students from working full-time alongside a full-time research commitment. They want to make sure that you’re working 100% on your PhD because balancing work isn’t easy.

Although it may not be banned in some institutions it is generally expected that students focus on their research and coursework full-time during a PhD and are therefore not typically able to hold down a full-time job.

Some programs may allow for part-time work, but it is generally not recommended as it can interfere with academic progress.

Additionally, many PhD programs offer funding in the form of stipends or fellowships which can help support students financially during their studies.

There are a few things to consider if you are thinking of working during your PhD.

The first is whether or not you will have enough time to dedicate to both your work and your studies. You don’t want your work to suffer because you are spending too much time on your PhD, or vice versa.

Another thing to consider is how working will affect your funding.

If you are receiving PhD funding or a scholarship from an external source, they may have stipulations on whether or not you can work while receiving their funding. Be sure to check with them before taking on any paid work.

Lastly, you will want to make sure that the work you are doing is related to your field of study. Working in a related field can help you with your research by giving you real-world experience that you can apply to your studies.

Even though some institutions allow you to work, should you?

Should you work during your PhD?

Some students feel that they need to work in order to support themselves during their PhD, while others feel that they can focus solely on their studies.

There are pros and cons to both approaches.

Working during your PhD can help you to cover your living expenses and may even allow you to save some money. However, it can also be a distraction from your studies and may make it more difficult to find time to do research.

I know that I wouldn’t be able to balance the pressures of a full-time job with my PhD studies and, therefore, decided to not have any jobs during my first year – this included jobs inside the University such as demonstrating in undergraduate laboratories.

Therefore, it is possible to do a PhD whilst working full-time but you really have to consider the impact of the extra pressures and commitments

. It is much easier to work alongside your PhD if you have a strong research-based masters degree and your job outside of your degree is flexible enough to allow you to attend different academic commitments such as attending seminars, meeting with advisers, and travelling to conferences.

Ultimately, the decision of whether or not to work during your PhD is up to you.

Consider your financial situation and how working would impact your studies before making a decision.

It can be difficult to juggle work and study commitments, and you may find yourself feeling overwhelmed and stressed. You may also have less time to socialize and enjoy your student life.

So, it’s important to think carefully about whether working during your PhD is right for you.

What type of work can you do during a PhD? Part time or Full time?

During your PhD there are a number of different options that you could consider if you want to (and you are allowed to) get a job.

I do not recommend working full-time alongside your PhD but, there are some options for part-time work to earn a little bit of money alongside your studies.

Full time work

My recommendation is that you do not try to fit a PhD alongside full-time work. Trying to work full time is asking for trouble.

There will be so many more pressures on you that it will not be a very enjoyable experience.

A PhD requires you to be creative.

Creativity comes from having enough mental space to allow your mind to connect new and interesting ideas together. If you are busy with work you will not have the mental capacity to be able to do this effectively.

Therefore, I recommend that you consider at least dropping down to part-time work if you are insistent on pursuing a PhD alongside employment.

I have seen PhD students complete a PhD part-time supported and partly funded by their current place of employment.

Part time work

If you want to know more about the best PhD student part-time jobs check out my full guide – click here for the full article.

There are a variety of part-time jobs that can easily supplement your income during a PhD.

The best PhD student part-time jobs will have flexible hours, provide you with a reasonable hourly rate, and not distract you from your primary goal of completing your PhD.

I have highlighted in my YouTube video, below, all of the different side hustles that PhD students can try if they need to earn a little bit more money.

The common part-time jobs for PhD students include:

• Hospitality
• Customer service
• University-based jobs – such as tutoring, marking exams, student services and working in laboratories
• Online jobs such as user testing, notetaker, and translating.
• Freelancing. Selling a skill that you have two people on services such as Upwork .

Why Should You Worry About Working During Your PhD

There are a number of reasons why you should worry about working during your PhD.

The most important is balancing workload, the fact that you were extending your time in academia by a significant amount, the increased risk of burnout, and ensuring you have enough resources to keep you going for multiple years.

A PhD is just like a full time job.

Therefore, getting a PhD while working full-time will be incredibly difficult. Both commitments will require at least 40 hours per week each.

Nonetheless, if you are able to have full flexibility on your work schedule and you are capable of distance learning for some part of your PhD it may be much more possible.

Many PhD students struggle with just the commitments of earning a doctorate. Consider working alongside your PhD very carefully.

Time it takes

A PhD will typically take between three and seven years. During this time it is extremely stressful and you need to make sure you’re capable of researching at your best for the entire time.

I have always said that a PhD is a marathon and not a sprint. Adding extra years to your PhD if you need to can be worth it. However, you must consider the amount of time it will take you to get your PhD and the potential return on that investment.

Unless you have a particular career secured or in mind for after your PhD the extra years it takes to complete a doctoral degree part-time are generally not worth it.

Burnout is a real consequence of doing a PhD.

By working alongside your PhD you’ll increase your chances of burnout significantly. This is true even if you like to study.

If you are prone to feelings of being overwhelmed I would stay away from earning a PhD whilst working full or part-time.

Slowly introduce part-time work if you need to once you have settled into the general routine of your PhD.

Tips for Earning Your PhD While Working

Here are a few general tips that may help you if you find yourself having to work alongside your PhD:

Talk to everyone involved

Everyone involved in this process needs to be on board. There will be times when you need to ask favours from your supervisor, colleagues, work supervisor or others.

Don’t be afraid to ask for help: Whether it’s from your supervisor, colleagues, or friends and family, don’t hesitate to ask for help when you need it.

This is not a sign of weakness, but simply a recognition that we all need assistance from time to time.

Stay Organized and on Track

Find a routine: Try establishing a set schedule for at least most days of the week and stick to it as much as possible. This will help you to stay focused and make the most of your limited time.

Get Involved in the Research Community

Remember to stay in touch with your research community.

Working part-time or full-time can mean that you miss out on the exciting recent advancements and collaboration with people in your field. Make an extra special effort to attend seminars, talks, and networking events to help progress your research and your academic career.

Don’t squirrel yourself away!

Work with your strengths

Know yourself: Be honest about how well you work under pressure and how much free time you realistically have.

If you know that you work better with a tight deadline, then try to structure your work schedule accordingly.

Personally, I need as much free mental space is possible to perform at my best. Just do what is best for you.

Wrapping up

This article has been through everything you need to consider if you are tempted by earning a PhD while working.

Your PhD programme may dictate whether it is possible to work alongside your PhD. Whether or not it is a good idea will be down to you and if you are able to balance an insane amount of commitments and work.

My general recommendation is that you should focus 100% on your PhD journey and although it is definitely possible you’re going to be at risk of burnout.

Combining part-time PhD’s, part-time jobs, and finding a flexible job that will help keep you focused on the primary goal of finishing your dissertation is the most sensible way of working alongside your PhD.

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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Mathematics Graduate Program

Thinking of applying to graduate school in mathematics.

Penn was ranked 8th among all US universities in a leading national study , and our mathematics graduate program was recently highest in a study of graduate programs in arts and sciences at Penn. We have a very active and involved mathematics faculty , diverse course offerings and a broad seminar schedule , with a variety of research projects and strengths in algebra, analysis, geometry-topology, combinatorics, logic, probability, and mathematical physics. We have a supportive atmosphere, with personal attention from the faculty and extensive interaction among graduate students. Our grad students can take courses not only in the Mathematics Department but also elsewhere at Penn, and the wide resources of the university are available. Our former graduate students have gone on to mathematical careers both in academia and in industry.

Our full-time Ph.D. students receive a generous and competitive support package including

• five years of funding with a combination of  fellowships and teaching assistantships;
• a stipend and a full tuition scholarship;
• no teaching responsibilities for at least two years (generally including the first and fourth year);
• health insurance coverage provided at no cost to the student.

We invite you to learn about our graduate program, our math department, our university and living in Philadelphia, a cosmopolitan city and a true mathematical hub, with easy access to nearby mathematics departments and research institutes.

We are looking for interested, mathematically talented and dedicated students to be a part of our group of excellent future mathematicians. Consider applying to Penn for your graduate education. Questions?

• Working While you Study for Your PhD

Written by Hannah Slack

It's possible to work during a PhD with careful time management. You might choose to do this if you need a job to help cover the cost of a postgraduate degree. Or, you may want to learn industry-based skills to benefit your future career. This page will take you through the different types of work PhD students often undertake, and the pros and cons of maintaining a job alongside such an intensive degree.

On this page

Can you work during a phd.

The simple answer is yes, you can work while studying a PhD and in fact, many do. The most common form of work is teaching during your PhD . But some students may also have part-time (or full-time jobs outside of the university).

Depending on the amount of work you plan to undertake, you will have to consider whether it would be better to do your PhD part-time or full-time. It’s highly unlikely you’ll be able to do a full-time job alongside a full-time PhD. However, it is possible to work part-time alongside a full-time PhD (or vice versa).

What type of work can you do during a PhD?

There are many different types of work PhD students can apply for. When someone says that they work alongside their PhD, most will assume that they have a stable, permanent contract. However, many PhD students work short-term contracts.

Contract work

The most common job for doctoral students is teaching undergraduates. Most departments will offer teaching opportunities to second-year and above researchers, paying you for training, seminar time, prep work and marking. Usually, you'll be able to decide how many seminar groups you wish to take on, allowing you to schedule work around your research. Teaching is an excellent chance to experience the other responsibilities that come with working in academia .

Another popular type of contract work is assistance roles . Many academics run outreach programmes that require more hours than they’re able to put in. Usually, emails will be sent around the departments advertising a short-term role. Jobs often include data entry, content management and research assistance. Again, these can be a great opportunity to build up workplace specific skills and receive a small financial boost.

Permanent roles

Some PhD students may also work more permanent roles. Often, self-funded students have to seek employment in order to financially afford tuition and living expenses. These students usually work part-time in industry . This can be both within and outside of the university. The types of roles students may undertake include admin, hospitality and even marketing. It’s a good idea to search for roles that match up with your skill set and future career goals .

Given the academic pressures of a PhD, many universities advise students not to work more than 16 hours a week . Otherwise, they may find themselves falling behind on a full-time PhD programme.

Pros and cons of working during a PhD

Working during a PhD can be a great opportunity to learn new skills and refine your current ones for future job applications. In fact, many Research Councils often require their funded students to undertake some form of work experience in order to build industry related skills.

However, managing a job on top of your own research can be stressful and limit the amount of free time you have available. Here are some of the most important pros and cons to consider before applying for a job.

• Gain more industry related experience
• Helps reduce financial pressure
• Regular forced breaks from your research can help refresh the mind
• Make connections with work colleagues, reducing the isolation often associated with PhD research
• Less time in the week to work on your PhD
• Schedule clashes could mean you miss out on academic opportunities, such as conferences
• Potential feelings of isolation from the academic community if you’re committed to an industry job

Tips for working during a PhD

#1 prioritise workload management.

The main thing to consider before applying for a job during your PhD is how you’re going to manage the workload. The PhD already comes with a hefty amount of work and so adding to that can cause additional stress.

The key is to set your priorities and manage your time effectively , taking regular breaks. Just like a job allows you to take holiday, do the same for your PhD. If the workload gets too much, be willing to consider the necessity of your job or whether it would be possible to reduce your PhD from full-time study to part-time .

#2 Talk to your supervisor

You should also discuss your situation with your supervisor so they’re aware of your wider responsibilities and time restraints. They’ll then be able to better advise on your progress. Additionally, you should make your industry boss aware of your PhD commitments. They too may be able to assist you. This might mean offering flexibility to your hours in case of last-minute academic events or allowing extended holiday to prepare for the viva .

#3 Don't forget to get involved in the research community

Working while studying can be time-consuming, but it's important to stay in touch with the wider research community nonetheless! Make sure you still find the time to attend conferences, seminars and networking events. This will help you form academic connections and get the most out of your doctorate.

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Ph.D. Program

Degree requirements.

In outline, to earn the PhD in either Mathematics or Applied Mathematics, the candidate must meet the following requirements.

• Take at least 4 courses, 2 or more of which are graduate courses offered by the Department of Mathematics
• Pass the six-hour written Preliminary Examination covering calculus, real analysis, complex analysis, linear algebra, and abstract algebra; students must pass the prelim before the start of their second year in the program (within three semesters of starting the program)
• Pass a three-hour, oral Qualifying Examination emphasizing, but not exclusively restricted to, the area of specialization. The Qualifying Examination must be attempted within two years of entering the program
• Complete a seminar, giving a talk of at least one-hour duration
• Write a dissertation embodying the results of original research and acceptable to a properly constituted dissertation committee
• Meet the University residence requirement of two years or four semesters

Detailed Regulations

The detailed regulations of the Ph.D. program are the following:

Course Requirements

During the first year of the Ph.D. program, the student must enroll in at least 4 courses. At least 2 of these must be graduate courses offered by the Department of Mathematics. Exceptions can be granted by the Vice-Chair for Graduate Studies.

Preliminary Examination

The Preliminary Examination consists of 6 hours (total) of written work given over a two-day period (3 hours/day). Exam questions are given in calculus, real analysis, complex analysis, linear algebra, and abstract algebra. The Preliminary Examination is offered twice a year during the first week of the fall and spring semesters.

Qualifying Examination

To arrange the Qualifying Examination, a student must first settle on an area of concentration, and a prospective Dissertation Advisor (Dissertation Chair), someone who agrees to supervise the dissertation if the examination is passed. With the aid of the prospective advisor, the student forms an examination committee of 4 members.  All committee members can be faculty in the Mathematics Department and the chair must be in the Mathematics Department. The QE chair and Dissertation Chair cannot be the same person; therefore, t he Math member least likely to serve as the dissertation advisor should be selected as chair of the qualifying exam committee . The syllabus of the examination is to be worked out jointly by the committee and the student, but before final approval, it is to be circulated to all faculty members of the appropriate research sections. The Qualifying Examination must cover material falling in at least 3 subject areas and these must be listed on the application to take the examination. Moreover, the material covered must fall within more than one section of the department. Sample syllabi can be reviewed online or in 910 Evans Hall. The student must attempt the Qualifying Examination within twenty-five months of entering the PhD program. If a student does not pass on the first attempt, then, on the recommendation of the student's examining committee, and subject to the approval of the Graduate Division, the student may repeat the examination once. The examining committee must be the same, and the re-examination must be held within thirty months of the student's entrance into the PhD program. For a student to pass the Qualifying Examination, at least one identified member of the subject area group must be willing to accept the candidate as a dissertation student.

• Doing a PhD in Mathematics
• Doing a PhD

What Does a PhD in Maths Involve?

Maths is a vast subject, both in breadth and in depth. As such, there’s a significant number of different areas you can research as a math student. These areas usually fall into one of three categories: pure mathematics, applied mathematics or statistics. Some examples of topics you can research are:

• Number theory
• Numerical analysis
• String theory
• Random matrix theory
• Graph theory
• Quantum mechanics
• Statistical forecasting
• Matroid theory
• Control theory

Besides this, because maths focuses on addressing interdisciplinary real-world problems, you may work and collaborate with other STEM researchers. For example, your research topic may relate to:

• Biomechanics and transport processes
• Evidence-based medicine
• Fluid dynamics
• Financial mathematics
• Machine learning
• Theoretical and Computational Optimisation

What you do day-to-day will largely depend on your specific research topic. However, you’ll likely:

• Continually read literature – This will be to help develop your knowledge and identify current gaps in the overall body of knowledge surrounding your research topic.
• Undertake research specific to your topic – This can include defining ideas, proving theorems and identifying relationships between models.
• Collect and analyse data – This could comprise developing computational models, running simulations and interpreting forecasts etc.
• Liaise with others – This could take many forms. For example, you may work shoulder-to-shoulder with individuals from different disciplines supporting your research, e.g. Computer scientists for machine learning-based projects. Alternatively, you may need frequent input from those who supplied the data for your research, e.g. Financial institutions or biological research colleagues.
• Attend a wide range of lectures, seminars and events.

Browse PhD Opportunities in Mathematics

Application of artificial intelligence to multiphysics problems in materials design, study of the human-vehicle interactions by a high-end dynamic driving simulator, physical layer algorithm design in 6g non-terrestrial communications, machine learning for autonomous robot exploration, detecting subtle but clinically significant cognitive change in an ageing population, how long does it take to get a phd in maths.

The average programme duration for a mathematics PhD in the UK is 3 to 4 years for a full-time studying. Although not all universities offer part-time maths PhD programmes, those that do have a typical programme duration of 5 to 7 years.

Again, although the exact arrangement will depend on the university, most maths doctorates will require you to first register for an MPhil . At the end of your first year, your supervisor will assess your progress to decide whether you should be registered for a PhD.

Additional Learning Modules

Some Mathematics departments will require you to enrol on to taught modules as part of your programme. These are to help improve your knowledge and understanding of broader subjects within your field, for example, Fourier Analysis, Differential Geometry and Riemann Surfaces. Even if taught modules aren’t compulsory in several universities, your supervisor will still encourage you to attend them for your development.

Most UK universities will also have access to specialised mathematical training courses. The most common of these include Pure Mathematics courses hosted by Mathematics Access Grid Conferencing ( MAGIC ) and London Taught Course Centre ( LTCC ) and Statistics courses hosted by Academy for PhD Training in Statistics ( APTS ).

What Are the Typical Entry Requirements for A PhD in Maths?

In the UK, the typical entry requirements for a Maths PhD is an upper second-class (2:1) Master’s degree (or international equivalent) in Mathematics or Statistics [1] .

However, there is some variation on this. From writing, the lowest entry requirement is an upper second-class (2:1) Bachelor’s degree in any math-related subject. The highest entry requirement is a first-class (1st) honours Master’s degree in a Mathematics or Statistics degree only.

It’s worth noting if you’re applying to a position which comes with funding provided directly by the Department, the entry requirements will usually be on the higher side because of their competitiveness.

In terms of English Language requirements, most mathematics departments require at least an overall IELTS (International English Language Testing System) score of 6.5, with no less than 6.0 in each individual subtest.

Tips to Consider when Making Your Application

When applying to any mathematics PhD, you’ll be expected to have a good understanding of both your subject field and the specific research topic you are applying to. To help show this, it’s advisable that you demonstrate recent engagement in your research topic. This could be by describing the significance of a research paper you recently read and outlining which parts interested you the most, and why. Additionally, you can discuss a recent mathematics event you attended and suggest ways in how what you learnt might apply to your research topic.

As with most STEM PhDs, most maths PhD professors prefer you to discuss your application with them directly before putting in a formal application. The benefits of this is two folds. First, you’ll get more information on what their department has to offer. Second, the supervisor can better discover your interest in the project and gauge whether you’d be a suitable candidate. Therefore, we encourage you to contact potential supervisors for positions you’re interested in before making any formal applications.

How Much Does a Maths PhD Typically Cost?

The typical tuition fee for a PhD in Maths in the UK is £4,407 per year for UK/EU students and £20,230 per year for international students. This, alongside the range in tuition fees you can expect, is summarised below:

Note: The above tuition fees are based on 12 UK Universities [1]  for 2020/21 Mathematic PhD positions. The typical fee has been taken as the median value.

In addition to the above, it’s not unheard of for research students to be charged a bench fee. In case you’re unfamiliar with a bench fee, it’s an annual fee additional to your tuition, which covers the cost of specialist equipment or resources associated with your research. This can include the upkeep of supercomputers you may use, training in specialist analysis software, or travelling to conferences. The exact fee will depend on your specific research topic; however, it should be minimal for most mathematic projects.

What Specific Funding Opportunities Are There for A PhD in Mathematics?

Alongside the usual funding opportunities available to all PhD Research students such as doctoral loans, departmental scholarships, there are a few other sources of funding available to math PhD students. Examples of these include:

You can find more information on these funding sources here: DiscoverPhDs funding guide .

What Specific Skills Do You Gain from Doing a PhD in Mathematics?

A doctorate in Mathematics not only demonstrates your commitment to continuous learning, but it also provides you with highly marketable skills. Besides subject-specific skills, you’ll also gain many transferable skills which will prove useful in almost all industries. A sample of these skills is listed below.

• Logical ability to consider and analyse complex issues,
• Commitment and persistence towards reaching research goals,
• Outstanding verbal and written skills,
• Strong attention to detail,
• The ability to liaise with others from unique disciple backgrounds and work as part of a team
• Holistic deduction and reasoning skills,
• Forming and explaining mathematical and logical solutions to a wide range of real-world problems,
• Exceptional numeracy skills.

What Jobs Can You Get with A Maths PhD?

One of the greatest benefits maths PostDocs will have is the ability to pursue a wide range of career paths. This is because all sciences are built on core principles which, to varying extents, are supported by the core principles of mathematics. As a result, it’s not uncommon to ask students what path they intend to follow after completing their degree and receive entirely different answers. Although not extensive by any means, the most common career paths Math PostDocs take are listed below:

• Academia – Many individuals teach undergraduate students at the university they studied at or ones they gained ties to during their research. This path is usually the preferred among students who want to continue focusing on mathematical theories and concepts as part of their career.
• Postdoctoral Researcher – Others continue researching with their University or with an independent organisation. This can be a popular path because of the opportunities it provides in collaborative working, supervising others, undertaking research and attending conferences etc.
• Finance – Because of their deepened analytical skills, it’s no surprise that many PostDocs choose a career in finance. This involves working for some of the most significant players in the financial district in prime locations including London, Frankfurt and Hong Kong. Specific job titles can include Actuarial, Investment Analyst or Risk Modeller.
• Computer Programming – Some students whose research involves computational mathematics launch their career as a computer programmer. Due to their background, they’ll typically work on specialised projects which require high levels of understanding on the problem at hand. For example, they may work with physicists and biomedical engineers to develop a software package that supports their more complex research.
• Data Analyst – Those who enjoy number crunching and developing complex models often go into data analytics. This can involve various niches such as forecasting or optimisation, across various fields such as marketing and weather.

What Are Some of The Typical Employers Who Hire Maths PostDocs?

As mentioned above, there’s a high demand for skilled mathematicians and statisticians across a broad range of sectors. Some typical employers are:

• Education – All UK and international universities
• Governments – STFC and Department for Transport
• Healthcare & Pharmaceuticals – NHS, GSK, Pfizer
• Finance & Banking – e.g. Barclays Capital, PwC and J. P. Morgan
• Computing – IBM, Microsoft and Facebook
• Engineering – Boeing, Shell and Dyson

The above is only a small selection of employers. In reality, mathematic PostDocs can work in almost any industry, assuming the role is numerical-based or data-driven.

How Much Can You Earn with A PhD in Maths?

As a mathematics PhD PostDoc, your earning potential will mostly depend on your chosen career path. Due to the wide range of options, it’s impossible to provide an arbitrary value for the typical salary you can expect.

However, if you pursue one of the below paths or enter their respective industry, you can roughly expect to earn [3] :

Academic Lecturer

• Approximately £30,000 – £35,000 starting salary
• Approximately £40,000 with a few years experience
• Approximately £45,000 – £55,000 with 10 years experience
• Approximately £60,000 and over with significant experience and a leadership role. Certain academic positions can earn over £80,000 depending on the management duties.

Actuary or Finance

• Approximately £35,000 starting salary
• Approximately £45,000 – £55,000 with a few years experience
• Approximately £70,000 and over with 10 years experience
• Approximately £180,000 and above with significant experience and a leadership role.

Aerospace or Mechanical Engineering

• Approximately £28,000 starting salary
• Approximately £35,000 – £40,000 with a few years experience
• Approximately £60,000 and over with 10 years experience

Data Analyst

• Approximately £45,000 – £50,000 with a few years experience
• Approximately £90,000 and above with significant experience and a leadership role.

Again, we stress that the above are indicative values only. Actual salaries will depend on the specific organisation and position and responsibilities of the individual.

Facts and Statistics About Maths PhD Holders

The below chart provides useful insight into the destination of Math PostDocs after completing their PhD. The most popular career paths from other of highest to lowest is education, information and communication, finance and scientific research, manufacturing and government.

Note: The above chart is based on ‘UK Higher Education Leavers’ data [2] between 2012/13 and 2016/17 and contains a data size of 200 PostDocs. The data was obtained from the Higher Education Statistics Agency ( HESA ).

Which Noteworthy People Hold a PhD in Maths?

Alan turing.

Alan Turing was a British Mathematician, WW2 code-breaker and arguably the father of computer science. Alongside his lengthy list of achievements, Turning achieved a PhD in Mathematics at Princeton University, New Jersey. His thesis titled ‘Systems of Logic Based on Ordinals’ focused on the concepts of ordinal logic and relative computing; you can read it online here . To this day, Turning pioneering works continues to play a fundamental role in shaping the development of artificial intelligence (AI).

Ruth Lawrence

Ruth Lawrence is a famous British–Israeli Mathematician well known within the academic community. Lawrence earned her PhD in Mathematics from Oxford University at the young age of 17! Her work focused on algebraic topology and knot theory; you can read her interesting collection of research papers here . Among her many contributions to Maths, her most notable include the representation of the braid groups, more formally known as Lawrence–Krammer representations.

Emmy Noether

Emmy Noether was a German mathematician who received her PhD from the University of Erlangen, Germany. Her research has significantly contributed to both abstract algebra and theoretical physics. Additionally, she proved a groundbreaking theorem important to Albert Einstein’s general theory of relativity. In doing so, her theorem, Noether’s theorem , is regarded as one of the most influential developments in physics.

Other Useful Resources

Institute of Mathematics and its Applications (IMA) – IMA is the UK’s professional body for mathematicians. It contains a wide range of useful information, from the benefits of further education in Maths to details on grants and upcoming events.

Maths Careers – Math Careers is a site associated with IMA that provides a wide range of advice to mathematicians of all ages. It has a section dedicated to undergraduates and graduates and contains a handful of information about progressing into research.

Resources for Graduate Students – Produced by Dr Mak Tomford, this webpage contains an extensive collection of detailed advice for Mathematic PhD students. Although the site uses US terminology in places, don’t let that put you off as this resource will prove incredibly helpful in both applying to and undertaking your PhD.

Student Interviews – Still wondering whether a PhD is for you? If so, our collection of PhD interviews would be a great place to get an insider perspective. We’ve interviewed a wide range of PhD students across the UK to find out what doing a PhD is like, how it’s helped them and what advice they have for other prospective students who may be thinking of applying to one. You can read our insightful collection of interviews here .

[1] Universities used to determine the typical (median) and range of entry requirements and tuition fees for 2020/21 Mathematics PhD positions.

• http://www.lse.ac.uk/study-at-lse/Graduate/Degree-programmes-2020/MPhilPhD-Mathematics
• https://www.ox.ac.uk/admissions/graduate/courses/dphil-mathematics?wssl=1
• https://www.graduate.study.cam.ac.uk/courses/directory/mapmpdpms
• https://www.ucl.ac.uk/prospective-students/graduate/research-degrees/mathematics-mphil-phd
• http://www.bristol.ac.uk/study/postgraduate/2020/sci/phd-mathematics/
• https://www.surrey.ac.uk/postgraduate/mathematics-phd
• https://www.maths.ed.ac.uk/school-of-mathematics/studying-here/pgr/phd-application
• https://www.lancaster.ac.uk/study/postgraduate/postgraduate-courses/mathematics-phd/
• https://www.sussex.ac.uk/study/phd/degrees/mathematics-phd
• https://www.manchester.ac.uk/study/postgraduate-research/programmes/list/05325/phd-pure-mathematics/
• https://warwick.ac.uk/study/postgraduate/research/courses-2020/mathematicsphd/
• https://www.exeter.ac.uk/pg-research/degrees/mathematics/

[2] Higher Education Leavers Statistics: UK, 2016/17 – Outcomes by subject studied – https://www.hesa.ac.uk/news/28-06-2018/sfr250-higher-education-leaver-statistics-subjects

[3] Typical salaries have been extracted from a combination of the below resources. It should be noted that although every effort has been made to keep the reported salaries as relevant to Math PostDocs as possible (i.e. filtering for positions which specify a PhD qualification as one of their requirements/preferences), small inaccuracies may exist due to data availability.

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Guide to Graduate Studies

The PhD Program The Ph.D. program of the Harvard Department of Mathematics is designed to help motivated students develop their understanding and enjoyment of mathematics. Enjoyment and understanding of the subject, as well as enthusiasm in teaching it, are greater when one is actively thinking about mathematics in one’s own way. For this reason, a Ph.D. dissertation involving some original research is a fundamental part of the program. The stages in this program may be described as follows:

• Acquiring a broad basic knowledge of mathematics on which to build a future mathematical culture and more detailed knowledge of a field of specialization.
• Choosing a field of specialization within mathematics and obtaining enough knowledge of this specialized field to arrive at the point of current thinking.
• Making a first original contribution to mathematics within this chosen special area.

Students are expected to take the initiative in pacing themselves through the Ph.D. program. In theory, a future research mathematician should be able to go through all three stages with the help of only a good library. In practice, many of the more subtle aspects of mathematics, such as a sense of taste or relative importance and feeling for a particular subject, are primarily communicated by personal contact. In addition, it is not at all trivial to find one’s way through the ever-burgeoning literature of mathematics, and one can go through the stages outlined above with much less lost motion if one has some access to a group of older and more experienced mathematicians who can guide one’s reading, supplement it with seminars and courses, and evaluate one’s first attempts at research. The presence of other graduate students of comparable ability and level of enthusiasm is also very helpful.

University Requirements

The University requires a minimum of two years of academic residence (16 half-courses) for the Ph.D. degree. On the other hand, five years in residence is the maximum usually allowed by the department. Most students complete the Ph.D. in four or five years. Please review the program requirements timeline .

There is no prescribed set of course requirements, but students are required to register and enroll in four courses each term to maintain full-time status with the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences.

Qualifying Exam

The department gives the qualifying examination at the beginning of the fall and spring terms. The qualifying examination covers algebra, algebraic geometry, algebraic topology, complex analysis, differential geometry, and real analysis. Students are required to take the exam at the beginning of the first term. More details about the qualifying exams can be found here .

Students are expected to pass the qualifying exam before the end of their second year. After passing the qualifying exam students are expected to find a Ph.D. dissertation advisor.

Minor Thesis

The minor thesis is complementary to the qualifying exam. In the course of mathematical research, students will inevitably encounter areas in which they have gaps in knowledge. The minor thesis is an exercise in confronting those gaps to learn what is necessary to understand a specific area of math. Students choose a topic outside their area of expertise and, working independently, learns it well and produces a written exposition of the subject.

The topic is selected in consultation with a faculty member, other than the student’s Ph.D. dissertation advisor, chosen by the student. The topic should not be in the area of the student’s Ph.D. dissertation. For example, students working in number theory might do a minor thesis in analysis or geometry. At the end of three weeks time (four if teaching), students submit to the faculty member a written account of the subject and are prepared to answer questions on the topic.

The minor thesis must be completed before the start of the third year in residence.

Language Exam

Mathematics is an international subject in which the principal languages are English, French, German, and Russian. Almost all important work is published in one of these four languages. Accordingly, students are required to demonstrate the ability to read mathematics in French, German, or Russian by passing a two-hour, written language examination. Students are asked to translate one page of mathematics into English with the help of a dictionary. Students may request to substitute the Italian language exam if it is relevant to their area of mathematics. The language requirement should be fulfilled by the end of the second year. For more information on the graduate program requirements, a timeline can be viewed at here .

Non-native English speakers who have received a Bachelor’s degree in mathematics from an institution where classes are taught in a language other than English may request to waive the language requirement.

Upon completion of the language exam and eight upper-level math courses, students can apply for a continuing Master’s Degree.

Teaching Requirement

Most research mathematicians are also university teachers. In preparation for this role, all students are required to participate in the department’s teaching apprenticeship program and to complete two semesters of classroom teaching experience, usually as a teaching fellow. During the teaching apprenticeship, students are paired with a member of the department’s teaching staff. Students attend some of the advisor’s classes and then prepare (with help) and present their own class, which will be videotaped. Apprentices will receive feedback both from the advisor and from members of the class.

Teaching fellows are responsible for teaching calculus to a class of about 25 undergraduates. They meet with their class three hours a week. They have a course assistant (an advanced undergraduate) to grade homework and to take a weekly problem session. Usually, there are several classes following the same syllabus and with common exams. A course head (a member of the department teaching staff) coordinates the various classes following the same syllabus and is available to advise teaching fellows. Other teaching options are available: graduate course assistantships for advanced math courses and tutorials for advanced undergraduate math concentrators.

Final Stages

How students proceed through the second and third stages of the program varies considerably among individuals. While preparing for the qualifying examination or immediately after, students should begin taking more advanced courses to help with choosing a field of specialization. Unless prepared to work independently, students should choose a field that falls within the interests of a member of the faculty who is willing to serve as dissertation advisor. Members of the faculty vary in the way that they go about dissertation supervision; some faculty members expect more initiative and independence than others and some variation in how busy they are with current advisees. Students should consider their own advising needs as well as the faculty member’s field when choosing an advisor. Students must take the initiative to ask a professor if she or he will act as a dissertation advisor. Students having difficulty deciding under whom to work, may want to spend a term reading under the direction of two or more faculty members simultaneously. The sooner students choose an advisor, the sooner they can begin research. Students should have a provisional advisor by the second year.

It is important to keep in mind that there is no technique for teaching students to have ideas. All that faculty can do is to provide an ambiance in which one’s nascent abilities and insights can blossom. Ph.D. dissertations vary enormously in quality, from hard exercises to highly original advances. Many good research mathematicians begin very slowly, and their dissertations and first few papers could be of minor interest. The ideal attitude is: (1) a love of the subject for its own sake, accompanied by inquisitiveness about things which aren’t known; and (2) a somewhat fatalistic attitude concerning “creative ability” and recognition that hard work is, in the end, much more important.

PhD Program

More information and a full list of requirements for the PhD program in Mathematics can be found in the University Bulletin .

During their first year in the program, students typically engage in coursework and seminars which prepare them for the  Qualifying Examinations .  Currently, these two exams test the student’s breadth of knowledge in algebra and real analysis. 

Starting in Autumn 2023, students will choose 2 out of 4 qualifying exam topics: 

• real analysis
• geometry and topology
• applied mathematics

Course Requirements for students starting prior to Autumn 2023

To qualify for candidacy, the student must have successfully completed 27 units of Math graduate courses numbered between 200 and 297.

Within the 27 units, students must satisfactorily complete a course sequence. This can be fulfilled in one of the following ways:

• Math 215A, B, & C: Algebraic Topology, Differential Topology, and Differential Geometry
• Math 216A, B, & C: Introduction to Algebraic Geometry
• Math 230A, B, & C: Theory of Probability
• 3 quarter course sequence in a single subject approved in advance by the Director of Graduate Studies.

Course Requirements for students starting in Autumn 2023 and later

To qualify for candidacy, the student must have successfully completed 27 units of Math graduate courses numbered between 200 and 297. The course sequence requirement is discontinued for students starting in Autumn 2023 and later.

By the end of Spring Quarter of their second year in the program, students must have a dissertation advisor and apply for Candidacy.

During their third year, students will take their Area Examination , which must be completed by the end of Winter Quarter. This exam assesses the student’s breadth of knowledge in their particular area of research. The Area Examination is also used as an opportunity for the student to present their committee with a summary of research conducted to date as well as a detailed plan for the remaining research.

Years 4&5

Typically during the latter part of the fourth or early part of the fifth year of study, students are expected to finish their dissertation research. At this time, students defend their dissertation as they sit for their University Oral Examination. Following the dissertation defense, students take a short time to make final revisions to their actual papers and submit the dissertation to their reading committee for final approval.

Throughout the PhD Program

All students continue through each year of the program serving some form of Assistantship: Course, Teaching or Research, unless they have funding from outside the department.

Our graduate students are very active as both leaders and participants in seminars and colloquia in their chosen areas of interest.

School of Science

Mathematical sciences, ph.d in mathematics.

Exploring New Theories at the Forefront of Mathematics and its Applications

Doctoral studies form our core graduate program.  The faculty in the department excel in numerous areas of applied mathematics and are well versed in many related disciplinary fields, thus they are highly qualified to train graduate students and mentor them in producing high-quality research and dissertations at the intersection of mathematics and the sciences or engineering.  Our Ph.D. training opens doors to research careers in academia, government laboratories, and industry and our department has a strong record of placing Ph.D. students in prestigious postdoctoral positions at top-tier universities and labs, and in industrial positions.

Students working for the doctorate must demonstrate high achievement both in scholarship and in independent research. All programs must follow the general rules of the Office of Graduate Education .

Program of Study

The Ph.D. degree results from following a program of study in mathematics or in applied mathematics.

Requirements

Students working for the doctorate must demonstrate high achievement both in scholarship and in independent research. All programs must follow the general rules of the Office or Graduate Education .

The student’s program of study must include:

• At least six, 4-credit (nonthesis) graduate mathematics courses (i.e., those with numbers MATH 6XXX or MATP 6XXX).
• At least one 3- or 4-credit course at the graduate (6000) level outside the department (i.e., not coded MATH or MATP and not cross-listed with any department course), selected in consultation with the math adviser.
• All doctoral students must pass a written preliminary exam as well as an oral qualifying examination and complete an oral candidacy presentation.

In addition, the course MATH 6591 Research in Mathematics is strongly suggested. Any deviations from these requirements must have the approval of the Department’s Graduate Committee.

The program catalog can be found here .

Resources frequently used by graduate students in Mathematics can be found here .

Program Outcome

Students who successfully complete this program will be able to:

• Demonstrate mastery of graduate-level courses covering a range of topics, including mathematical analysis, mathematical methods and modeling, computational mathematics, and operations research.
• Demonstrate mastery of graduate-level courses in at least one area outside of mathematics.
• Conduct high-quality original research on a topic in mathematics or applied mathematics with results suitable for journal publications and technical presentations.
• Read and interpret research level articles in mathematics and develop new mathematical concepts.
• Develop mathematical formulation and solution of scientific problems from a range of disciplines.
• Communicate sophisticated mathematical ideas and concepts concisely and effectively in both oral and written form.

Financial Aid

There are several potential ways that a Math Sciences graduate student can get financial support while enrolled at RPI. The most common methods are:

Fellowships

There are many opportunities for students to obtain fellowships to support their graduate studies.  These fellowships can come from inside or outside the department or the Institute.  For example, recent fellowships have been available from the Department of Education, and there are competitive fellowships available from the National Science Foundation. The specifics of fellowships vary from year to year, and the  Graduate Student Coordinator has information on available fellowships and application procedures.  Your academic advisor in the department is another good source of information about fellowships. You should be sure to consider that the Math Sciences Department has guidelines for continuation of support for doctoral students. Graduate students who receive full support from the Department should plan to complete their doctoral programs within four or five years. Students can expect that their support will continue through this period, provided that they continue to make satisfactory progress toward their degree and they continue to perform well in their teaching assignments. "Satisfactory progress" means completing courses, required examinations (preliminary, qualifying, and candidacy), selecting a research area, and making progress toward completing a thesis. If a student requires support beyond the fifth year, each situation will be considered individually.

Teaching Assistantship

Teaching Assistant (TA) assignments vary significantly throughout Rensselaer Polytechnic Institute. In the Department of Mathematical Sciences, TA-ships are typically one-year appointments that cannot be extended for more than two years. They tend to be of two types. TA-ships can take the form of independent classroom teaching (often called recitations), which may include small lectures, problem solving, computer labs, grading, office hours, etc. The TA works with a TA Supervisor, who is the faculty member teaching the course. The vast majority of TA-ships are of this form. A few teaching assistantships take the form of grading and office hours only. All TA’s are required to participate in RPI’s TA Training Program as well as the Department’s TA Orientation prior to their first semester of teaching. In addition, all TA’s must attend the TA Seminar before or during their first semester of teaching at RPI. A graduate student, the Math Sciences Department Master TA, typically teaches this one-credit course (graded as Satisfactory/Unsatisfactory). The topics of this course vary according to the needs of the participants. In the past they have included: Maple, Grading, Laptops, Composing Quizzes, Campus Resources, Academic Honesty, Proctoring Exams, Extra Help, Office Hours, Latex, Making a Syllabus, etc.  In addition, each TA has their class visited and feedback is provided.

Research Assistantship

Many faculty in the Math Sciences Department have research funding that can be used to support graduate students who are interested in doing research in their field. The Research Assistantships (RA’s) do not typically have any teaching component. This allows a graduate student to have more time to work on Master’s or Ph. D. research. This is a topic you may want to talk to your advisor about.

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How can I know if I am working fast enough to finish my PhD?

I am a second-year math Ph.D. student without a master's degree who likes taking math classes and loves TAing and tutoring, but dislikes research. During the first semester of my second year, I spent about 3-4 hours a week on research while taking three classes and got very little done research-wise.

I usually have about 8 hours of office hours a week for TA (some of which are for review or practice sessions) and I also type LaTeX notes for students which I improve each semester.

I want to be a lecturer or community college math instructor (I am aware that their pay/ job security is not ideal, but I would rather have a job that I enjoy than being a professor, because I definitely do not want to continue research after I graduate with my Ph.D.) It seems like for those positions completing a Ph.D. is needed to be a competitive job candidate, but the quality of the research doesn't really matter. Being more likely to get a job is the only reason I am doing my Ph.D.

I have gotten 6 A's and 3 A+'s in the 9 classes I have taken for my Ph.D. so far, because I spend enough on them and also tutor other graduate students in some of the classes I am taking with makes me spend even more time. I like how for classes I learn everything by going to lectures instead of having to read references, have a large number of small homework problems instead of a small number of difficult research problems, and taking tests instead of working on long-term projects.

During my second year the main reason I don't do much research-wise is that I spend too much time TAing and tutoring, but starting in my third year it will be more so motivation than time, because I won't be taking classes. I was supposed to read a lot over winter break and also complete a proof and didn't have any other responsibilities. I read what I was supposed to and typed about 40 pages of LaTeX notes, but I skipped all of the proofs in the book and I found that after I finished reading I did not remember most of the notes I had typed, because I typed them quickly and didn't read the text deeply. I also didn't do the proof. I had a lot of time during break, but I chose not to do a good job because I wasn't motivated.

How can I determine whether I am doing enough each week to complete my Ph.D. and not be kicked out for not doing enough research? I usually don't finish the weekly assignments I get, but my advisor hasn't mentioned that I am not doing enough. I know that one way to find out is to ask my advisor, but I don't want them to know that I just want to complete the Ph.D. And I don't want to spend time on research, because then they might decide that they don't want to work with me anymore and I wouldn't be able to finish my Ph.D.

I think that I probably wouldn't spend more than 12 hours weekly during my last three years on research, but I would spend at least 8 hours weekly even if less than that was enough. I know that this would leave me with a significant amount of free time which I would probably spend tutoring undergraduate and graduate students, because I am motivated to do that and it would help improve my teaching skills.

• Is 12 hours spent weekly on research for the final 3 years enough to complete a minimal Ph.D. for a slightly above average Ph.D. student?
• Is there a way that I can continually check whether I am on track to complete my Ph.D. and not be kicked out for making unsatisfactory progress?
• community-college

• 3 Is this in the US? –  Buffy Commented Jan 8, 2022 at 22:30
• 28 We can't convert X hours a week to "you'll get your PhD." If your advisor thinks you're not doing enough, then they are probably correct. –  Azor Ahai -him- Commented Jan 8, 2022 at 22:32
• 9 Not to be a damper on this, but if you asking how many hours a week it takes, then you may not be suitable to do it. –  Tom Commented Jan 9, 2022 at 17:51
• 10 1 - 'but dislikes research' ?! –  BCLC Commented Jan 10, 2022 at 0:19
• 5 It sounds like you don't even like reading math, which is a bit odd for somebody who likes taking math classes. To be clear, skipping over all the proofs in a book you were supposed to read means you read approximately 0% of the mathematical content of the book. –  Kevin Carlson Commented Jan 10, 2022 at 22:41

7 Answers 7

In my opinion, the bare minimum required to have a good chance to complete a PhD in math is to actually want one . By that I mean, not just to want a diploma, or a practical means towards having some specific type of career, but to actually have a decent level of passion and enthusiasm about the idea of doing research in math, which is what doing a PhD is all about.

A person who lacks this level of passion and enthusiasm will most likely fail. Even many who do have it will not succeed. A math PhD is already a hard enough thing to do for those who find the idea of math research appealing, so that I really wouldn’t recommend it to anyone who doesn’t find it appealing.

Basically, it’s a bad idea to think in terms of the number of hours per week. If you are thinking in those terms it strongly signals that you lack intrinsic motivation for doing what it takes to get a PhD. And without intrinsic motivation, your chances simply don’t look very good. Keep in mind that I don’t know you and what you’re capable of, but that’s at least the generic answer I would give for someone in your situation.

See also this answer with some related thoughts. And best of luck with your studies going forward.

• Comments are not for extended discussion; this conversation has been moved to chat . –  Bryan Krause ♦ Commented Jan 11, 2022 at 15:22

I applaud your love of learning mathematics and your interest in teaching. That said, you will have to be both thoughtful and lucky to reach your career goal: teaching relatively advanced mathematics in a relatively stable job. Many of the other answers here point to the difficult job market now and in years to come.

You do need that PhD. If your advisor is sympathetic to your goals and you are good enough to do some research they may be able to help you choose a problem in your Goldilocks zone: one you're genuinely interested in answering, hard enough to be worth solving but not so hard that it's beyond your abilities.

I don't think you can succeed as long as you are counting and regretting the research hours. You really do have to care about the problem. Look for one in an area that does not call for lots of technical machinery before you can even state and understand open questions. If there's something touching the things you like to teach, go for that.

In your eventual job search consider secondary schools. You will find some advanced topics and some good students to mentor.

Your advisor is the best person to answer this, but it doesn't sound like you are headed for success. A PhD is normally all about research, though in the US (assumed), in the second year, there is a lot of coursework normally. Only after passing comprehensive exams does the research get serious and dominates your time (and maybe your life).

At the point you are, if the program is 6-7 years in total, you are probably ok, but eventually research will be closer to 20-30 hours per week for someone who is also also a TA.

Talk to your advisor about your progress and maybe look around at what your peers are doing as well.

However, research, by its nature, can't be predicted. Some projects take much more time (per week and overall) than others. It is because the unknown is unknown and you are trying to make it known. There are no guarantees.

And, if you hate research, you should reexamine your path. Even the coursework you are proud of now won't be terribly valuable as community college faculty. It is meant to prepare you for serious research (and for passing qualifiers).

• I agree that I don't think they care about my course grades when hiring me but if I am looking to be a lecturer or community college instructor (not a community college professor) do they care about the quality of the thesis? –  user152100 Commented Jan 8, 2022 at 22:51
• Also the program is 5 years in total so I have 3 years of research after this year. –  user152100 Commented Jan 8, 2022 at 22:58
• 2 @user152100: do they care about the quality of the thesis --- If they care at all, I suspect the general topic of the thesis would be much more relevant than the quality (e.g. other things equal, this would probably help more than this ), especially since probably no one will likely to be able to judge the latter, and even if they were able to judge and the thesis was very high quality, red flags might be raised over why you are applying there. –  Dave L Renfro Commented Jan 9, 2022 at 6:43

My two cents as someone who switched from pure math to applied math and actually found it interesting and motivating enough to complete my PhD:

It's not ideal that you don't have that passion obviously. But a change of scenery can help. I was always a pure math person and was trying to do PDE in my early years of PhD (things like Vlassov-Poisson-Boltzmann, proving existence etc) but found it very hard to stay motivated. I came clean to my advisor and we parted ways and I had decided that I would leave the program with a Master's. But a new hire at my university was said to be hiring her first PhD students and wants to do research in computational neuroscience, so I figured it would not hurt to meet her.

Now after two papers and two years, I am about to defend this May and have found it very interesting to do Machine Learning/Data Science stuff so I will get hired for that kind of role for a company. This is because the math/simulation part of the research involves a fair bit of Reinforcement Learning algorithms.

Our department is huge and I heard similar stories of students completing their math PhD's in applied/computational/inter-disciplinary area and do a lot of simulation/coding as opposed to the old-fashioned 8hrs a day pure math stuff. I also enjoy teaching and my research workload has never exceeded more than 20 hrs a week (on average about 15 - the math is really not that hard in this field). If I did more, then I probably would have published at premium journals and get a strong chance at a nice postdoc, but I am okay with that.

But all of this depends on your advisor and in my case she is the nicest and most supportive teacher I have ever had.

• me too, I found myself less-motivated in pde. –  M.K Commented Jun 27 at 17:17

The "publish or perish" injunction reflects a professional reality in most of academia

Since different PhD candidates differ enormously in their skills and the quality of their work, there is no magic formula to convert hours of work to success or lack thereof. Your advisor and your broader supervisory panel should be able to give you feedback on whether you are on-track in your program. Normally, by this stage of your candidature you would have set some research milestones that progress towards completion of your dissertation. If your panel are doing their job well then you should not be substantially behind schedule without knowing about it, but it never hurts to ask if you're unsure. As to being kicked out for lack of progress, PhD programs have milestones and (at least) annual reviews where you are rated on your progress. Students who have not made sufficient progress to pass their review are generally given at least a semester to catch up, but there are mechanisms to remove them from the program if they are persistently behind and not making research progress. You should read the program rules at your institution to see the review system that is in place in your PhD program.

As to your broader career goal and strategy, there are a few aspects of this that are a bit naive and are perhaps cause for concern. Firstly, even for teaching-heavy positions, universities/colleges have generally adopted the view that research scholarship is an important marker of knowledge in a field, and they will prefer their students to be taught by scholars with a substantial research record. For this reason, many academics who focus on teaching undertake research relating to pedagogical aspects of their discipline, as a means to demonstrate high levels of knowledge in the field. (You will find that many acadmics who are heavily focused on teaching will publish papers in teaching journals.) Secondly, there are a number of scholars who believe that universities are entering a period of slow decline (see e.g., Reynolds 2012 ), which may portend a highly competitive market for academic positions in the future. As shown in the figure below, the rate of produced PhD graduates is substantially above the number of new academic positions, and the gap is getting larger over time. (This figure is only for science and engineering but other fields have been similar.)

As a result of these various norms and trends, academics who do not publish research generally have a hard time in academia, even in teaching-heavy positions. Moreover, this is only likely to intensify as more qualified PhD graduates compete for relatively fewer (or perhaps even absolutely fewer) academic positions. You say that you are okay with the low pay and job insecurity of teaching positions at a community college, but on present labour market trends this might become quite extreme.

New faculty position vs new PhDs in science and engineering (Figure 1, Shillebeeckx, Maricque and Lewis 2013 )

Consider your backup plan

In addition to the other answers, assuming you succeed in getting the Ph.D. with minimal effort put in on the research (I have trouble seeing how this is possible), consider what might happen if you tried for a lecturer position but didn't get one, or you tried community college teaching and it didn't work out. Many people in this scenario would then look toward a job in industry, and here I'm afraid having a Ph.D. may hurt you significantly given your specific situation. It's my understanding that having a Ph.D. usually communicates to hiring managers that you're skilled at self-directed research (and of course implies then that you actually like it or at least don't hate it). So if you manage to get a Ph.D. without either of these being true, it may hurt your chances of getting a job in industry that matches your interests should your current plan of being a non-research lecturer or community college instructor not work out. Something to consider.

Are you sure you hate research?

Alternatively, are you sure you that really hate research? Is it possible that you simply haven't found the right research area or problem? Maybe a different area in math, or maybe something altogether different? One test that may be helpful in answering this question is this: are you happiest doing the same thing day in and day out and doing it well (year after year after year), or do you need variety, to learn and do something new every few months or years? Are you a naturally creative person or do you find it difficult? For example, if you like to cook, do you enjoy inventing your own recipes or do you enjoy cooking from established recipes without deviating from them? Basically I'm trying to get at whether you have a personality that is creative, likes to invent and discover, and that craves novelty, or one that seeks to excel at doing one thing and doing it well in a highly stable environment. If the former, I suggest that maybe you've either not truly given research a chance (or may not even have a full picture of what research is), or may not have found a topic you're passionate enough about yet that you want to help push the envelope in that area. If the latter (you crave stability over novelty), then research probably truly isn't for you and that's fine. It's important to learn what type of job best fits you. In this case my gut feeling is a Ph.D. is probably not going to work out, and even if it does it may cause you problems down the road.

Other answers gave some good advices on the matter. First, if you really don't like research then the psychological load will get intensified every day, to the point that it might become unbearable. So please be careful about what you really like and dislike before it gets too late.

The other point is, I think not liking research and being in academia are two paradoxical features. Maybe you haven't found your passion yet, and the courses you have dealt with so far weren't your taste. So at this point, the 2nd year of PhD, you should keep trying to find what you really like. There's still plenty of time for that IMO.

At the end, if you still think that research is not your thing, then maybe you could try some part-time teaching to, for example, high school students. That is a good starting point to check whether your passion and future is in teaching. I think having a PhD is not a necessity for teachers in most places of the world. And I doubt that teaching at high school is less fun than being a college tutor.

• +100 for suggestion to try part time teaching. OP is taking a pretty big gamble right now. It's definitely worth seeing if what they're working for is even something they'll want. –  bob Commented Jan 11, 2022 at 16:37

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How challenging experiences led me to pursue a PhD in Mathematics by Shanise Walker

As a student graduating high school, I was convinced of one thing: I was going to be a high school mathematics teacher. Everything I had done in high school and the inspiration and encouragement I received from teachers, family, and friends helped me feel reassured that my decision was the right one. As a high school student, I excelled in every subject, but doing mathematics was a passion. My love for mathematics led me to tutoring both middle school and high school students in mathematics, participating in mathematics competitions, and learning about other areas of mathematics outside of the curriculum. I had even earned the highest achievement award every year for mathematics in my grade level, so being a high school mathematics teacher seemed like the perfect choice for me.

As an undergraduate student, I immediately declared that I wanted to be a mathematics education major. Although I would have to be accepted into the program, I was sure of my choice in major. Completing the requirements to get into the program were easy because I was eager to be a math teacher. For the first few years of undergrad, things were going well. I added the mathematics major to my degree program and became a double major in mathematics and mathematics education. I was accepted into the mathematics education program and was set on my goals; everything was going well.

Fast forward to the spring semester of junior year, something changed. While taking a math education course focused on technology in the classroom, I found myself in a situation that I could not explain and one that could not be explained to me at the time. One of the first assignments in the course was to write an argumentative essay on technology in the classroom and its benefits or hindrances. When I wrote my essay, I focused my attention on the hindrances and how too much technology could lead students to rely heavily on devices and not enough on understanding the concepts. In the end, I received a low score on this assignment and when I inquired about the low score, the teaching assistant responded, “It’s just wrong.” This was just the beginning of a long battle of receiving low grades because “it’s just wrong.” Those words haunted me, so I stopped inquiring and just accepted the grades. I received lower grades than my peers, even on assignments where we had the same answers. I really disliked going to that class, but I knew I needed to finish the course because it was a requirement for my mathematics education degree. The real test came during the group final project. The project consisted of a group paper and a class demonstration on teaching a math topic to students. For the group paper, my group scored near perfect, but on the class demonstration, I scored significantly lower than my classmates. My group members and I did not understand it since I had written over half of the group paper and the project idea was one that I had brought to the group. I spent countless hours working on this project only to get near perfect or perfect grades on the group graded portion of the project but a low grade on my individual portion.

After receiving the group project grade, I had had enough. I decided to meet with the instructor of the course about my grades and my displeasure with the course. During our meeting, I asked the instructor to explain to me why my grades were much lower than classmates, especially on assignments where we had the same answers. It was then that I learned that this was not about my work, but about who I am. The professor outright admitted that the teaching assistant had given me lower scores because I was Black. The professor was already aware of the situation and had been for semesters before I became a student in his course. It had happened to other Black students who had taken the course before me. I was given assurance that while my grades were low, my final grade would not be. When I left that meeting, I cried. I was angry. While I knew that the particular teaching assistant would not be a grader for any other courses I would take in the major, I felt that I no longer had a place of belonging in that major. Despite feeling like I didn’t belong, I still had a passion for teaching high school mathematics, so I was determined to complete the degree.

The determination to continue with my mathematics education degree would change while I was a participant in an 8-week summer REU mathematics program. When I arrived at the REU program, I had no knowledge of how to conduct mathematics research and I was also unsure of what exactly I would be researching. However, with good mentorship from my research mentor and a postdoctoral student (now a tenured faculty member), I found myself interested in mathematics beyond teaching it. I was interested in solving math problems and I found that sense of community during the REU program that was lacking in my home department. Within the first few weeks of the REU program, I had decided that I wanted to get a PhD in mathematics–a thought I had not had before. My research mentor gave me advice on preparing and applying to graduate school. I took the advice and applied for PhD mathematics programs.

When I returned to my university the fall after the REU program, I was still pursuing a double major in mathematics and mathematics education. I knew that I had only one semester of coursework before I would be student teaching, but there was some unrest in me in continuing my mathematics education degree. I had just come from spending an entire summer doing math research, and I had this motivation in me to pursue a PhD. A week before classes started, I dropped my remaining mathematics education courses. After dropping the courses, I found myself in the position of being able to graduate at the end of the semester since I needed only one mathematics course and one elective course in a certain area to graduate. However, I decided I wanted to stay the entire senior year, so I enrolled in two mathematics courses and other electives.

While I dropped my mathematics education courses, I did not immediately drop my mathematics education major because I was still a bit torn about the idea of perhaps not being able to teach high school mathematics. However, before the fall semester ended, I went for it. I dropped the major and pursued my newfound interest of getting a PhD in mathematics. I started on a research project with a faculty member in the mathematics department and began submitting applications for graduate school. I submitted a number of applications for PhD in mathematics programs before the Thanksgiving break, so everything was going well.

In the spring of my senior year, I had another incident that solidified my pursuit of a mathematics PhD. I attended a graduate school fair at my institution to learn about other graduate programs at other institutions. While doing so, I stumbled upon a master’s program in mathematics education and thought to myself: “Well, maybe I could get my teaching certification while in this program because after all, I still had a passion to teach high school mathematics.” The program was at an institution close to my hometown, so that also meant that I would be able to spend more time with my family. The deadline to apply to the master’s program had not yet passed, so I thought to myself I would give it a shot. I spoke with the program’s representative, and we discussed the program and my GRE scores. She told me that I would likely get into the program with probationary status due to my GRE composite score. When I told her I had already been accepted into PhD programs in mathematics, there was a bit of shock on her face (and I am sure on mine as well). What I knew to be true was that my GRE Verbal Reasoning score was not as high, but I had done well on the GRE Mathematics portion. The composite score missed the mark for their institution to be granted full admission, so with this information in mind, I did not apply to the program. I continued with my plan to get a PhD in mathematics and finally decided that teaching high school mathematics was not the best fit for me. The following fall, I went off to graduate school, pursuing a mathematics PhD program at the same institution I had done the REU. Six years later, I completed the program and earned a PhD in mathematics.

Now, as I write about this experience almost ten years later, for the first time I ask myself, “How can eight weeks change the whole course of your life?” This is exactly what the REU program did for me. It changed the course of my life. It gave me a mathematical experience that I had not encountered before. It provided me with the mentorship I needed to succeed and gave me a sense of belonging in the mathematics community that I had not felt before. It also provided me with motivation to pursue something different–a doctoral degree. For this, I am grateful.

Two years ago, I had an opportunity to fulfill my passion of teaching high school mathematics. I taught calculus to a group of underrepresented minority students at a STEM summer program for high school students. This experience was just as joyful as I thought it would be, and I will always cherish it.

1 Response to How challenging experiences led me to pursue a PhD in Mathematics by Shanise Walker

Hi, I’m really inspired by your passion ,persistence and clarity to stick on to take up mathematics at research level. Currently I’m doing my ph. D program in management in India. But having graduated in bachelor’s degree in maths, I now have ardent desire to continue my masters and then proceed to do ph. d in maths. Though it’s 30 years since I lost touch, your life story is still furthering my passion. Thanks and a nice flow of narrative. Regards, Soundra

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ORIGINAL RESEARCH article

Phd-supervisors experiences during and after the covid-19 pandemic: a case study.

• 1 Department of Education, University of Bergen, Bergen, Norway
• 2 Department of Educational Studies in Teacher Education, Faculty of Education, Inland Norway University of Applied Sciences, Hamar, Norway
• 3 Department of Psychosocial Science, University of Bergen, Bergen, Norway
• 4 Faculty of Arts and Physical Education, Volda University College, Volda, Norway

Introduction: The COVID-19 pandemic has significantly impacted the education sector, and this case study examined nearly three hundred PhD supervisors in Norway. The study was driven by the urgent need to better understand the professional, social, and existential conditions faced by doctoral supervisors during extended societal shutdowns. This explorative case study builds on a former study among PhD candidates and investigates the experiences of doctoral supervisors when remote work, digital teaching, and digital supervision suddenly replaced physical presence in the workplace, largely between March 12, 2020, and autumn 2022, due to the COVID-19 pandemic.

Methods: A mixed-methods research approach, incorporating formative dialog research and case study design, was employed to bridge the conceptual and contextual understanding of this phenomenon. The primary data sources were a survey ( N = 298, 53.7% women, 46.3% men, response rate 80.54%) and semi-structured interviews (with nine PhD supervisors). Supplementary data collection was based on formative dialog research. It included field dialog (four PhD supervision seminars), open survey responses ( n = 1,438), one focus group ( n = 5), an additional survey ( n = 85), and document analysis of PhD policy documents and doctoral supervision seminar evaluations ( n = 7). The survey data, interview data, focus group data, and supplementary data focus also retrospectively on the first year of the pandemic and were collected from August 2022 until October 2023.

Results: The findings from the explorative case study revealed that the PhD supervisors faced numerous challenges during the pandemic, both professionally and personally. For PhD supervisors who extensively worked from home over a long period, the situation created new conditions that affected their job performance. These altered conditions hindered their research capacity, their ability to follow up with their PhD candidates, and their capacity to fulfill other job responsibilities. Although the PhD supervisors received some support during the pandemic, it seems that the incremental measures provided were insufficient.

Discussion: The case study results indicate that it is more important than ever to understand the gap between the formulation, transformation, and realization arenas when distinguishing between incremental, semi-structural changes and fundamental changes in PhD regulations and guidelines brought on by societal crises. This highlights the need for better crisis preparedness at the doctoral level in the years to come.

1 Introduction

Effective doctoral supervision is crucial for guiding PhD candidates through the complexities of their research, ensuring academic rigor and the successful completion of their dissertations ( Bastalich, 2017 ; Wichmann-Hansen, 2021 ; Kálmán et al., 2022 ). The role of PhD supervisors during the pandemic and their impact on educational quality at various levels has been an under-researched area both nationally and internationally ( Börgeson et al., 2021 ; Krumsvik et al., 2022 ). Supervisors who have varying experiences and work under diverse conditions are key players in the transformation arena where central policies are applied at the institutional level. Their interaction with PhD-candidates, whether in-person or remotely, shapes partly the quality of PhD-programs and candidates’ learning experiences. The COVID-19 pandemic has influenced the education sector in numerous ways, and this case study examined nearly three hundred PhD-supervisors in Norway with a Mixed Method Research design and different methods and data. The impetus for the study was the urgent need for a better knowledge base to understand the professional, social, and existential conditions for doctoral supervisors when society is shut down for an extended period. This explorative case study builds on our former study among PhD-candidates ( Krumsvik et al., 2022 ) and investigates the experiences of doctoral supervisors when remote work, digital teaching, and digital supervision suddenly replaced physical presence in the workplace (to varying extents).

First, the introduction contextualizes the study; second, the methodology is described; third, the main part presents the results from the survey part of the study; fourth, the data from the interviews and Supplementary data are presented; fifth, the discussion and conclusion are presented.

International policy documents underline the importance of PhD-supervision [ European University Association (EUA), 2010 , 2015 ] and, in Norway, it is crucial to view PhD supervision considering the specific frame factors for the PhD’s and some general trends of changed frame factors in doctoral education over the last 10 years ( Krumsvik, 2016a , 2017 ). It is therefore important to examine such frame factors in light of PhD-supervisors’ experiences during the pandemic, but the current state of knowledge is still limited around this topic. However, “The United Kingdom Research Supervision Survey Report 2021″ found that among the 3,500 PhD supervisors in the United Kingdom, 65% felt that supervisory responsibilities have increased during the pandemic, 32% agreed that “concerns over supervision have kept me awake at night over the last 12 months” and 31% agreed that “supervising doctoral candidates makes me feel anxious over the last 12 months” ( UK Council for Graduate Education, 2021 ). With these abovementioned issues in mind, this doctoral supervision study builds on our previous research on doctoral-level education ( Krumsvik and Jones, 2016 ; Krumsvik and Røkenes, 2016 ; Krumsvik et al., 2016a , b , 2019 , 2021 ; Krumsvik et al., 2022 ) and aims to examine the experiences of PhD supervisors in Norway during the pandemic to answer the research questions below:

1. To what extent has the COVID-19 pandemic impeded the PhD supervisors’ frame factors on the micro-level, and how do they perceive this situation?

2. To what extent has the COVID-19 pandemic influenced PhD supervisors’ frame factors on the meso-level, and how do they perceive this situation?

3. How do the PhD-supervisors experience the more general aspects of their supervision role during and after the pandemic?

1.1 The Norwegian context

To contextualize the research questions to the Norwegian context, one must remember that doctoral candidates in Norway are not students per se but are employees (on a 3–4 years contract) and more regarded as colleagues than students, and in this sense, the roles are more equal than in traditional supervisory relationships at a lower level (supervisor-student). Both by having PhD fellows being considered highly competent adult employees with state employment contracts, where they receive regular salaries, and have regular offices, they are initially part of the work community found within academia with its routines, duties, and rights. Another contextual aspect is that Norwegian PhD-candidates defend their theses relatively late in their careers. The average age for a candidate’s defense is between 37 and 38 years and higher for many candidates within the humanities and social sciences. In comparison, the median age across OECD countries is 29 ( Sarrico, 2022 , p. 1304). Table 1 provides a generalized comparison of doctoral education across Nordic countries, the UK, and the US ( Andres et al., 2015 ; Burner et al., 2020 ). While such broad overviews might exaggerate differences, they provide a framework for understanding doctoral education on a spectrum. This spectrum ranges from countries with significant government influence, where PhD candidates are employed (e.g., Nordic countries), to countries with moderate government influence, where PhD candidates are not employed (e.g., the UK), and finally to countries with minimal government influence, where PhD candidates are also not employed (e.g., the US). Despite these variations, the global trend indicates that doctoral education is becoming increasingly dependent on external funding ( Bengtsen, 2023 , p. 45).

Table 1 . Overview of the Nordic PhD model in comparison to UK and US models.

In addition, women defend their theses on average 2 years later than men. Taking into account that the average age for first-time mothers in Norway is now 30.1 years, there is a lot that needs to happen within a few years, and this may sometimes affect the feasibility of their PhD-projects. This can, e.g., be related to the gender differences in Norway about parental leave days during the pandemic which is much higher for women than for men at the universities ( Krumsvik et al., 2022 ) 1 . Another contextual factor that distinguishes doctoral supervision from other supervision (at lower levels) is that over 90% of the doctoral theses in Norway are article-based theses ( Krumsvik, 2016b ; Mason and Merga, 2018 ; Solli and Nygaard, 2022 ), which implies 3–4 published articles and an extended summary or synopsis (a “kappe” in Norwegian, ranging between 50 and 90 pages). This means that the PhD-candidates receive “supervision” and feedback from approximately 8–10 referees in scientific journals on their articles, in addition to feedback from their PhD supervisors. Because of this, many PhD-supervisors are co-authoring their doctoral candidates’ publications. A final contextual aspect is the recent studies indicating a decrease in doctoral disputations nationwide in Norway over the past two years ( Steine and Sarpebakken, 2023 ) – probably as a consequence of the pandemic. In a survey, Ramberg and Wendt (2023 , p. 22) found that about 60 percent of PhD candidates and 50 percent of postdoctoral candidates ( N = 300) were delayed during the autumn of 2022. The study showed that illness or leave, often due to caregiving responsibilities during the pandemic, was the most common reason for delays among PhD candidates and postdoctoral candidates, particularly impacting women more than men. Following illness, reduced access to supervisors, empirical data, research facilities, and external partners were significant factors contributing to delays in their research activities. Nearly a third of delayed candidates reported reduced access to supervisors, and about a fifth faced issues with external partner access, highlighting the critical role of these resources in completing research projects. When it comes to the PhD-supervisors, more specifically, the supervision differs from other types of supervision in that a formal PhD agreement is signed with a binding supervisor contract that lasts for 3–4 years (the PhD period) and is signed by both the supervisor and the candidate. The supervisor also has an overarching responsibility to avoid delays and ensure that the PhD program can be completed within the standard time frame. Supervisors are primarily responsible for guiding doctoral candidates on the specific, content-related aspects of their projects. This includes helping candidates identify the knowledge frontier in their field, position their study within the research field, develop clear and consistent research questions, choose appropriate scientific and methodological approaches, and provide expert guidance in discussing results and addressing ethical issues related to the thesis. This obviously places relatively high competence requirements on the supervisors, both in terms of their academic and research skills, and in relation to the doctoral supervision itself, as poor or inadequate supervision at this level can expose the candidate to a certain “drop-out risk” in the project.

Maintaining education quality during the COVID-19 pandemic has been challenging due to the widespread shift to digital teaching, supervision, and remote work. Many university teachers were unaccustomed to the online, digital learning environment, working with PhD candidates remotely for extended periods. Some taught in hybrid settings, with some PhD candidates quarantined at home while others attended in-person classes. Additionally, others navigated ordinary learning contexts with COVID-19 precautions like masks and social distancing. This situation altered frame factors, adding complexity to the discussion of education quality.

Considering this, the case study seeks to understand if, and potentially how, external factors in pedagogical contexts over which institutions, academics, and teachers have no direct control play out. Lindensjö and Lundgren (2014) find that such external factors might have a significant impact on the outcomes of educational training, teaching, and supervision. Therefore, it is crucial to contextualize the pandemic experiences among PhD supervisors with respect to these factors, as they imply national and institutional frames for their PhD supervision. Though there exist several quantitative, survey-based studies on the impact of COVID-19 on PhD supervision (e.g., Pyhältö et al., 2023 ; Löfström et al., 2024 ), there is still a lack of in-depth qualitative understanding of the impact of COVID-19 on the supervisory relationship. The studies of Löfström et al. (2024) and Pyhältö et al. (2023) indicated that supervisors faced significant challenges in identifying when PhD candidates needed assistance and providing adequate support for their well-being during the shift to remote supervision. Supporting the progress and wellbeing of full-time candidates, who were more adversely affected by the pandemic than their part-time peers, became increasingly difficult. The increase in email communications could overwhelm supervisors, exceeding manageable levels and complicating their ability to offer timely and effective feedback. The lack of spontaneous, informal conversation, previously facilitated by in-person meetings, further hindered their ability to monitor and support the candidates effectively. These challenges were particularly pronounced for supervisors in scientific fields requiring lab work and practical training, which were severely disrupted by the pandemic, and supporting the progress and wellbeing of full-time candidates, who were more adversely affected by the pandemic than their part-time peers, became increasingly difficult. Furthermore, supervisors reported that their PhD candidates’ lack of a scholarly community and inadequate supervision were significant challenges. This reflects the supervisors’ view that the availability of a supportive research environment and adequate supervision are critical for candidates’ success ( Pyhältö et al., 2023 ). The study by Pyhältö et al. (2023) also found that supervisors generally estimated the impact on candidates’ progress and well-being to be more negative than the candidates themselves did, which may imply that supervisors have a broader perspective on the long-term consequences of disruptions like the COVID-19 pandemic. Research prior to the pandemic ( Pyhältö et al., 2012 ) has shown that apart from the importance of having clear and long-term financing, proper research facilities, and sufficient time to pursue a PhD, supervisors also stress the significance of PhD candidates’ motivation, self-regulation, efficacy, and engagement as essential personal regulators for success in the PhD process.

1.2 Theoretical framework

This case study is exploratory and intrinsic ( Stake, 1995 , 2006 ), utilizing an abductive approach to theory with frame factor theory as our theoretical framework ( Lundgren, 1999 ; Lindensjö and Lundgren, 2014 ). Frame factor theory suggests that society’s influence on education manifests through a target system, an administrative system, and a legal system. This theory, used in educational sciences and pedagogy, acts as a lens for planning and analysis, positing that external factors, beyond the control of institutions and educators, significantly affect educational outcomes. We will further explain the contextual application of frame factor theory in this case study below.

Previous research highlights a gap in (doctoral) education between the formalization and realization arenas in frame factor theory ( Lindensjö and Lundgren, 2014 ; Krumsvik et al., 2019 ). Linde (2012) introduces a transformation arena between these two, explaining the difficulty of implementing measures in complex organizations like universities. There is rarely a straightforward relationship between central decisions (formulation arena or macro-level) and their implementation (realization arena or micro-level). Policy documents require interpretation and application by faculty leaders, PhD program leaders, supervisors, and PhD candidates (transformation arena or meso-level) ( Linde, 2012 ).

Given this context, a main focus of this case study was to evaluate how Norwegian PhD supervisors managed changed frame factors and education quality during the pandemic. The Norwegian Agency for Quality Assurance in Education (NOKUT) defines education quality as “the quality of teaching classes, other learning facilities, and students’ learning outcomes in terms of knowledge, skills, and general competence” ( Skodvin, 2013 , p. 2). It is important to differentiate between educational quality, study quality, and teaching quality.

Education quality is a broad concept encompassing everything from the subject/study program level to the government’s education policy. In contrast, study quality is narrower, referring specifically to the educational institution ( Skodvin, 2013 , p. 3). Teaching quality goes further to the micro-level, focusing on course quality, teacher effectiveness, and PhD supervision. This study examined how PhD supervisors experienced COVID-19 restrictions at the micro- and meso-levels, considering two of the three levels. Figure 1 illustrates the analytical lenses in this mixed methods research (MMR) and formative dialog research case study:

Figure 1 . The analytical focus in the case study ( Krumsvik et al., 2019 ) is based on the frame factor theory ( Linde, 2012 ; Lindensjö and Lundgren, 2014 ).

2 Methodology

To understand and corroborate conditions faced by doctoral supervisors related to COVID-19 extended societal shutdowns, both in breadth and in depth, we employed a mixed-methods research design, combining quantitative data to show the strength of associations and qualitative data to explore their nature ( Johnson et al., 2007 ; Creswell and Plano Clark, 2017 ). We utilized a three-stage design, QUAL-QUANT-QUAL (qualitative-driven sequential design, Schoonenboom and Johnson, 2017 ), making it a qualitative-dominant mixed-methods study ( Johnson et al., 2007 , p. 124). Using mixed methods research allowed us to explore the complex research problem more comprehensively compared to using either quantitative or qualitative data alone. Though the approach is less common in case studies ( Tight, 2016 , p. 380), the mixed methods are increasingly used (e.g., Ertesvåg et al., 2021 ; Hall and Mansfield, 2023 ; Peters and Fàbregues, 2023 ). Advocates of such approaches consider mixed methods to “complement and extend one another and thus lead to better descriptions, clearer explanations and an enhanced understanding of phenomena, research aims and questions” ( Ertesvåg et al., 2021 , p. 655).

Specifically, an exploratory, sequential mixed-methods design was used to address the research questions ( Fetters et al., 2013 ; Creswell and Plano Clark, 2017 ). This design involves collecting and analyzing qualitative data first (QUAL), using those findings to guide the quantitative data collection and analysis in the second phase (QUANT), and then using the quantitative results to inform further qualitative data collection and analysis in the third phase (QUAL). This method integrates through building, where results from one phase inform the next.

We conducted a cumulative data collection and analysis process ( Creswell and Guetterman, 2021 ), basing survey questions on previously collected data from field dialogues, online observations, seminar evaluations, and document analysis. The questionnaire consisted of a general demographic questions (e.g., gender, educational background and what field(s) the supervisor supervised in), in addition to a range of multiple response items addressing four key themes: (1) important factors to complete a PhD, (2) supervisor challenges, (3) working from home experiences, and (4) perceived need for future competences as supervisors. Finally the questionnaire contained a range of statements measured on a Likert-scale from 1 to 5 where 3 was neutral (e.g., to what extent do you feel that your PhD-candidate(s) are on track with their doctoral project?). The qualitative interview guide ( Kvale and Brinkmann, 2015 ) was developed from the prior quantitative data (survey), and the focus group guide was based on earlier survey and qualitative interview data (see Figure 2 below). We integrated research questions, methods, interpretation, and reporting at various points, using narratives where qualitative and quantitative results are presented in different sections of the same article through the contiguous approach ( Fetters et al., 2013 ). This article primarily examines the coherence between qualitative and quantitative findings based on confirmation , expansion , or discordance ( Fetters et al., 2013 ). The approach used in the study is similar to Hall and Mansfield (2023) and the coherence is derived from joint displays using visual means.

Figure 2 . The research process. The yellow arrows show the main data sources, and the blue arrows show the Supplementary data in this article. In addition, we have conducted focus group interviews and an extra survey, which will be published in another article (since they mainly focus on academic writing with the large language models).

As a consequence of the mixed-methods design, this study combines two approaches in case study research. The first, proposed by Stake (1995 , 2006) and Merriam (2009) and Merriam and Tisdell (2016) , is situated in a social constructivist paradigm, and is attached to the qualitative part (connected to the second part of each research question). The second, based on Eisenhardt (1989) , Flyvbjerg (2011) , and Yin (2012) , approaches the case study from a post-positivist perspective ( Hyett et al., 2014 , p. 1) (connected to the first part of each research question). This intrinsic case study ( Stake, 1995 ) aims to focus on ecological validity:

“Ecological validity is the degree of correspondence between the research conditions and the phenomenon being studied as it occurs naturally or outside of the research setting” ( Gehrke, 2018 , p. 563). Informant selection was based on a purposeful method ( Maxwell, 2013 ), in which we recruited PhD supervisors from Norway.

Next, all interviews were analyzed using reflexive thematic analysis ( Braun and Clarke 2019 , 2021 ) where themes were constructed and presented in this paper (see section 4). In addition, we also conducted a sentiment analysis ( Dake and Gyimah, 2023 ) of the nine interviews (see Supplementary file ).

To answer the research question, we combined formative dialog research ( Baklien, 2004 ) and case study research ( Stake, 2006 ). Data collection consisted of fieldwork (see Supplementary file ), a survey N = 298, 53.7% women, 46.3% men, response rate 80.54%, nine semi-structured interviews (with PhD supervisors), and one focus group ( N = 5). Supplementary data consisted of an additional survey ( N = 85), PhD-policy document analysis ( N = 6), field dialogues (4 PhD supervision seminars), open survey data (1,438 responses), seminar observations ( N = 4), and reviews of relevant documents such as evaluations of doctoral supervisor seminars. We also used policy documents and regulations concerning PhD education in Norway as supplementary sources.

We focused on how PhD supervisors experienced changing frame factors, such as university lockdowns, remote work, digital teaching, digital supervision, doctoral progression, and others, with an emphasis on illuminating the micro-level (course and teaching level) from the PhD supervisors’ perspective. This focus is twofold: the program’s structure and quality directly affected the PhD- supervisors during the pandemic. The second is simply that they conducted several evaluations about matters related to the structure and quality compared with the others. However, PhD- candidates’ opinions are also important, and their views are also interwoven because some of them have been present during field dialogs and participated in the PhD-supervision seminars.

When focusing on how PhD-supervisors experience their supervision, PhD’s research progression, psychosocial aspects, their nearest superior, and the main focus are on illuminating the meso-level (institutional and program level).

2.1 Cumulative research process

In our case study, we brought the experiences and our study among PhD’s ( Krumsvik et al., 2022 ) from the period March 12, 2020, to November 30, 2021, into our design of this study. We executed an excessive cumulative data collection process (including a part during the pandemic) and analysis, especially from August 2022 – October 2023. The relatively long time period allowed the researchers to test their interpretations along the way and detect contrary evidence, e.g., reach saturation during the coding and analysis of the qualitative data ( Creswell and Guetterman, 2021 ).

3.1 Quantitative part (survey)

Above and below are the results of the quantitative part of the study, based on the survey data. This analysis is tentative and covers only the survey results. The interview data and Supplementary data will be presented later in the paper. Two hundred and forty respondents completed the survey ( N = 298, 80.54% response rate). The academic backgrounds of the supervisors were diverse, with the three largest groups coming from natural sciences, humanities, education and teacher training. The largest group of supervisors (41.75%) supervised PhD candidates in education and teacher training (see Table 2 ).

Table 2 . Distribution of supervisors by academic background and PhD supervision in various fields.

A narrow majority (58.08%) of the supervisors had submitted an article-based dissertation (see more in attachment 5 in the Supplementary file ), in the Supplementary file meaning that approximately four out of ten supervisors have not “hands on” experience with article-based thesis as their thesis in their own doctoral degree. A large majority (81.67%) had supervised PhD candidates before and after the pandemic, while 11.67% had only supervised during and after. 41.27% of the supervisors stated that the coronavirus pandemic (from March 12, 2020 - January 2022) had impeded their candidate(s) progress in their doctoral project. 21.12% agreed (to a large or very large extent) that the PhDs’ publication process of articles to scientific journals has been delayed because of the journal’s peer review process during the pandemic (i.e., journal processing times seemed to increase due to several factors including a lack of available peer reviewers because of heavy workloads, health issues, more teaching, etc.).

3.1.1 Challenges in supervision

Results in Table 3 indicate that the most commonly reported challenges faced by supervisors during the pandemic were balancing work and family life and working from home, each affecting more than a third of the supervisors. Psycho-social aspects, such as loneliness, also emerged as a notable challenge. The cancelation of conference participation and stays abroad were significant issues, reflecting the broader impact on professional development opportunities. Concerns about supervision quality were also prominent. Some supervisors reported no challenges, highlighting a degree of variability in experiences. Other challenges included delays in the peer review process for journals, difficulties with publishing, and issues related to research ethics, though these were less commonly reported.

Table 3 . Challenges faced by supervisors during the pandemic in terms of supervision.

3.1.2 Challenges in working from home

Results in Table 4 indicated that supervisors faced multiple challenges while working from home during the pandemic. The most common issue was having little contact with colleagues, which affected more than six in ten supervisors. Supervisors also frequently reported having little contact with their PhD candidates. Distractions from others at home were another prevalent challenge. Many supervisors experienced an increased workload due to digital teaching from home, and lacking office equipment, such as desks and office chairs, was also commonly reported. Psycho-social aspects, such as loneliness, were significant issues as well. The lack of space and increased home responsibilities, such as childcare, were notable challenges. A smaller number of supervisors reported having no challenges at all. Other less commonly reported issues included limited access to library services and poor internet access.

Table 4 . Challenges faced by supervisors during the pandemic working from home.

3.1.3 Factors PhD candidates need to complete their doctorate

We find that there is a high degree of consistency between what supervisors ( Table 5 ) and PhD candidates ( Table 6 ) consider to be the most important factors for completing the doctorate. In particular, it is persistence, resilience, and the ability to work independently are the most important factors, in addition to supervision and co-writing with supervisors.

Table 5 . Most important factors in completing a PhD as reported by PhD supervisors.

Table 6 . Most important factors in completing a PhD as reported by PhD Candidates.

Thus, there is considerable agreement between what the supervisors and the PhD candidates report, which may indicate that within the academic tradition, the doctoral journey is primarily seen as an individual endeavor (feat of strength) where the supervisor is the closest supporter.

3.1.4 Appreciation of supervision

The supervisors mostly agreed that both they and the PhD candidates value supervision. 89.91% responded they agree or strongly agree to this question for themselves, and 92.47% responded they agree or strongly agree on behalf of the PhD candidates. In comparison, 61.25% responded similarly to whether the department values supervision, while 24.17% were neutral, and 14.59% responded they disagree or strongly disagree. This may suggest that the supervisory relationship is primarily between the PhD candidate and the supervisor, with less firm ties to the institution.

When it comes to what extent the supervisors think that their institution has been accommodating regarding compensating the loss of progress due to the coronavirus pandemic for their own PhDs, 27.2% stated that this had been done to a small extent or very small extent and 29.39% stated that this had been done to a large extent or very large extent. 30.1% agreed (large extent and very large extent) that supervisory responsibilities have increased during the pandemic. 13.3% expressed (to a large or very large extent) that supervising doctoral candidates makes them feel anxious’ over the last 24 months” (pandemic), but the majority (64.3%) experienced this to a small and very small extent. 9.3% expressed (to a large and a very large extent) that concerns over doctoral supervision have kept them awake at night over the last 24 months (pandemic), but the majority (69.3%) experienced this to a small and very small extent. 56.1% of the supervisors have not discussed any challenges with the progress of their doctoral candidate(s) project due to the coronavirus pandemic with the department’s human resources manager/head.

When asked how many hours they have enshrined in their working plan per semester as the main supervisor per PhD candidate, supervisors state this varies from zero to above 80 h, but for the majority, it is between 20 and 40 h per semester (40.46%). 23.1% state they do not think that their PhD-candidate(s) are on track with their doctoral project, while 50.2% state that their PhD-candidate(s) are on track with their doctoral project. Some PhDs publish their articles in their thesis based on pre-collected data (e.g., as a part of bigger projects), while others publish their articles in their thesis based on data collections done by themselves. 58.77% of the supervisors think this affects the completion time for the last group of PhDs (large and very large extent). 53.4% of the supervisors have been co-authoring their doctoral candidates’ publications.

3.1.5 What competencies supervisors need

As seen from Table 7 , nearly half of the supervisors believed they needed more pedagogical and methodological competence related to supervision. Additionally, about one-third felt they lacked knowledge about formal aspects, such as guidelines, related to the PhD program. The supervisors reported that the guidelines for the doctoral program were somewhat clear, particularly those for article-based dissertations. This perceived clarity was positively correlated ( r = 0.23, p = 0.002) with the extent to which the institution offered “continuing professional development” (CPD), and 39.88% of the supervisors stated that their institution did not provide supervisors with CPD. Thus, while many supervisors recognized the need for enhanced pedagogical and methodological skills, as well as a better understanding of formal guidelines, the availability of CPD programs was associated with clearer doctoral program guidelines. This suggests that increasing access to professional development opportunities could improve supervisors’ competence and clarity regarding program requirements, ultimately benefiting the supervision process.

Table 7 . Competencies PhD supervisors believe they need to increase.

3.1.6 Female academics with children

About four out of ten supervisors (41.07%) agreed (to a large or very large extent) that female PhDs with children seem to have more home responsibilities than men (e.g., for childcare, household, homeschooling, own children in quarantines, etc.) during the pandemic. About three out of ten (27.77%) agreed (to a large or very large extent) that female PhDs’ (with own children) submission rates to scientific journals have been delayed as a consequence of COVID-19, considering that women seem to have more home responsibilities (e.g., for childcare, household, homeschooling, own children in quarantine, etc.) during the pandemic. About two out of ten (23.64%) agreed (to a large or very large extent) that female supervisors’ (with their own children) submission rates to scientific journals have been delayed as a consequence of COVID-19, considering that women seem to have more home responsibilities (e.g., for childcare, household, homeschooling, own children in quarantine, etc.) during the pandemic.

Cronbach’s alpha ( α = 0.87) indicated a high level of consistency among three statements concerning the increased home responsibilities faced by female researchers with children compared to their male counterparts during the pandemic. These statements highlighted that female researchers with children appeared to bear more responsibilities at home, such as childcare, household tasks, and homeschooling, and as a result, their submission rates to scientific journals had been adversely affected by COVID-19. The average response (mea n = 3.18, standard deviatio n = 0.88) indicated that the supervisors were generally neutral toward these statements. However, closer inspection revealed that female supervisors (mea n = 3.29, standard deviatio n = 0.92) agreed with these statements more than male supervisors (mea n = 3.03, standard deviatio n = 0.79), a difference that was statistically significant ( p = 0.017) but with a small effect size (Cohen’s d = 0.30). There was a positive correlation ( r = 0.23, p = 0.002) between whether the PhD candidate had considered quitting the PhD program and the three statements, which suggests that supervisors who reported that PhD candidates had considered quitting also agreed more with the statements. Conversely, a negative correlation ( r = −0.21, p = 0.002) was found between considering quitting the PhD program and the belief that the institution made sufficient efforts to compensate for the lack of progress during the pandemic, indicating that better institutional support might have reduced the likelihood of candidates considering quitting.

3.2 Qualitative part (interview data and other types of qualitative data)

We conducted a cumulative data collection process where the qualitative interview guide questions were built upon previously collected quantitative data (survey). Based on a snowballing sample ( Patton, 2015 ), we recruited nine doctoral supervisors from the humanities, social-, and educational sciences with diverse experience and approaches to supervising PhD candidates during the pandemic. Using semi-structured interviews ( Brinkmann, 2022 ), each supervisor was interviewed online using Zoom with interviews lasting from 30 to 60 min. All interviews were conducted in Norwegian and later transcribed verbatim. We followed Braun and Clarke’s, (2019 , 2021) approach to reflexive thematic analysis to analyse the interview data. The themes constructed from the analysis of the interview data focus issues, such as “The Impact of the Pandemic on Supervision,” “Home Office Experience,” Workload and Employer Support,” “PhD Candidate Preparation for Article-Based Theses,” “Competence in Supervising Article-Based Theses,” and “Guidelines and Structuring the PhD Process.”

3.2.1 Analyzing the interview with Kyle

Introduction: Kyle, aged 47, specializes in professional ethics. He completed his doctoral degree through a monographic thesis and is relatively new to supervising PhD candidates, currently guiding three, two of whom he is the main supervisor.

Impact of the Pandemic : Kyle wore two hats during the pandemic: as a PhD supervisor and as a leader of a doctoral program. He noted that the pandemic did not significantly impact his supervisees due to well-planned data collection that adapted to digital formats when necessary. His role as the program leader gave him broader insights into how other candidates fared, with some experiencing difficulties in recruiting interviewees and needing to adjust their research plans accordingly.

PhD Supervision During the Pandemic : Kyle’s supervision was largely unaffected by the pandemic as most of it was conducted digitally, catering to students located in different parts of the country. He emphasized the importance of maintaining frequent contact, especially when usual social and professional gatherings were suspended. The pivot to online platforms like Zoom and increased digital communication tools helped maintain the continuity and quality of supervision.

Home Office Experience : Working from home was generally positive for Kyle, who appreciated the reduced distractions and the ability to maintain productivity with a well-equipped home office. However, he missed informal interactions with colleagues, which were hard to replicate through digital means.

Workload and Employer Support : Kyle experienced a slight increase in workload as more effort was required to monitor and support students remotely. His interactions with his Head of Department/direct manager were supportive, helping him navigate the challenges of remote supervision.

PhD Candidate Preparation for Article-Based Theses : Kyle observed that many PhD candidates were unprepared for the intricacies of article writing, including the lengthy processes of submission and peer review. He attributed this to their educational background, which primarily focused on monographic work at the bachelor’s and master’s levels.

Competence in Supervising Article-Based Theses : Although Kyle has not written a synopsis (‘kappe’, i.e., a synthesis chapter for article-based theses) himself, he feels prepared due to his involvement in supervisor training programs that include synopsis writing. He believes in collaborative supervision where co-supervisors with more experience in specific areas can complement his guidance.

Guidelines and Structuring the PhD Process : Kyle praised the clarity of guidelines regarding the synopsis writing at his program, highlighting proactive efforts to discuss and understand these guidelines among candidates and supervisors. He supports the idea of starting the synopsis early in the PhD journey, allowing candidates to develop a clear perspective on how their articles will integrate into their larger thesis narrative.

Summary: Kyle’s approach to PhD supervision during the pandemic was proactive and adapted to the challenges of remote interactions. He emphasizes the importance of clear guidelines, structured support from the academic program, and the benefits of collaborative supervision. His perspective offers valuable insights into managing PhD supervision under crisis conditions and highlights areas for potential improvement in preparing candidates for the demands of article-based theses.

3.2.2 Analyzing the interview with Sally

Introduction: Sally, aged 46, is experienced in the field of educational sciences and professional research, having supervised 15 PhD candidates to completion. She conducted her doctoral research through an article-based thesis.

Impact of the Pandemic on PhD Candidates : Sally observed that the pandemic had a limited impact on most of her PhD candidates, except for 2–3 individuals who experienced delays, partially due to the pandemic. Disputations were delayed for some candidates who preferred physical attendance, affecting their completion timeline.

Adaptations in Supervision Methods: The pandemic made Sally diversify her supervision methods, including more frequent digital meetings with Zoom or Teams and asynchronous communications like email. She shifted from paper-based to digital comments on drafts, which enhanced the efficiency and immediacy of feedback. This change is something she intends to continue using beyond the pandemic.

Home Office Experience: Sally found working from home manageable and returned to the office as soon as feasible, particularly because she needed to balance work with family responsibilities. The transition to the home office did not significantly disrupt her supervision activities, though it introduced minor challenges like occasional distractions from family.

Increased Workload During the Pandemic: Sally reported a slight increase in her workload during the pandemic due to a need for more frequent communication to ensure the continuity and quality of supervision. This was compounded by the timing of her candidates being in critical phases of their thesis work.

Support from Employer: She felt that the focus of her institution’s support during the pandemic was more on ensuring that PhD candidates were well-supported rather than directly supporting the supervisors themselves.

Preparedness of PhD Candidates: Sally noted that while the PhD candidates were generally well-prepared academically, they often lacked specific training in writing article-based theses, a significant adjustment from writing monographic theses typical at the bachelor’s and master’s levels.

Competence in Supervising Article-Based Theses: Sally felt confident in her ability to supervise article-based theses despite recognizing the ongoing need to adapt and learn, particularly in managing the synthesis chapter or “kappen.”

Clarity of Guidelines for the Synopsis: She found the guidelines for writing the synopsis at her institution clear and involved in educational efforts to help candidates understand these guidelines better. However, she questioned whether standardization would improve understanding or unnecessarily restrict academic freedom.

Timing for Writing the Synopsis: Reflecting on her experience and current practices, Sally advocated for thinking about the synopsis early in the doctoral process but cautioned against producing extensive texts prematurely. She emphasized the importance of adapting the scope of the synopsis as the research evolves.

Use of Doctoral Committees’ Guidelines: Sally observed that adherence to guidelines varies depending on whether committee members are national or international, with international members often impressed by the candidate’s ability to publish in high-ranking journals.

Overall, Sally’s experiences and insights provide a nuanced view of PhD supervision during the pandemic, highlighting flexibility, adaptation, and the importance of maintaining high standards of communication and support. Her approach demonstrates a balance between structured guidance and allowing academic independence, aiming to foster resilience and adaptability among her PhD candidates.

3.2.3 Analyzing the interview with Gabbie

Introduction: Gabbie, aged 54, specializes in school and teacher education. She has supervised two PhD candidates to completion and is currently guiding four others. Her doctoral thesis was article-based.

Impact of the Pandemic on PhD Candidates : Gabbie observed varied impacts of the pandemic on her PhD candidates. While two of her students were minimally affected, one faced significant challenges in data collection due to difficulties in recruiting informants. This disparity seems to have been influenced by the candidates’ approaches or perhaps their personal rapport with potential informants.

Changes in Supervision Practices: The pandemic shifted Gabbie’s supervision to entirely online formats using Zoom, Teams, or phone apps. While she was accustomed to digital interaction, the lack of informal, face-to-face interactions led to a more formal and structured supervision style. The spontaneous “corridor conversations” that often enhance relational aspects of supervision were missing, which she felt detracted from the personal connection in the supervisor-supervisee relationship.

Home Office Experience: Gabbie had a positive experience working from home, finding it efficient and beneficial due to eliminating commute times and the conducive environment at home for focused work. Her family setup supported this arrangement well, allowing her to balance work and home life effectively during the pandemic.

Workload Changes During the Pandemic: Her workload in terms of PhD supervision remained roughly the same, though the nature of interactions changed. Instead of impromptu office drop-ins, there were more scheduled meetings, primarily online via Zoom or Teams, which required a different kind of preparation and possibly led to more structured discussions.

Support from Employer: Gabbie noted a lack of specific support for supervisors from her employer during the pandemic; the focus was more on ensuring that she, like other staff, was generally coping with the pandemic’s challenges. There was an emphasis on looking out for the PhD candidates’ well-being, translating into a directive for supervisors to maintain close contact and support.

Preparedness of PhD Candidates for Article-Based Theses: Similar to Kyle and Sally, Gabbie agreed with the survey findings that many candidates are not well-prepared for writing article-based theses. She attributes this to their academic background, which primarily focuses on monograph writing. She advocates for collaborative writing for the first article to help familiarize candidates with the process of scholarly writing and peer review.

Evaluation of Own Competence in Supervising Article-Based Theses: She feels confident in her supervisory skills but acknowledges that continuous learning and discussion with peers are essential for handling complex or unfamiliar issues that arise during supervision. Gabbie appreciates the collaborative nature of the supervisory teams at her institution, which helps in managing any gaps in her experience or knowledge.

Clarity of Guidelines for the Synopsis: Gabbie finds the guidelines for writing the synopsis to be somewhat unclear and open to interpretation, suggesting that more explicit guidelines could help, especially for those new to supervising or external committee members who evaluate the theses.

When to Start Writing the Synopsis : She recommends that PhD candidates consider the synopsis throughout their doctoral journey but compile it towards the end. Gabbie advises keeping a file of potential content for the synopsis from the start of the doctoral process, which can include discarded sections from articles or ideas that do not fit into the articles but are valuable for the overarching thesis narrative.

Overall, Gabbie’s experience reflects a pragmatic and flexible approach to PhD supervision. She adapts to the demands of the pandemic while trying to maintain the quality of academic mentorship. Her strategies for managing remote supervision and her positive attitude toward the enforced changes highlight a successful adaptation to the challenges posed by the pandemic.

3.2.4 Analyzing the interview with Henrik

Introduction: Henrik, aged 46, specializes in school and educational research. He has successfully guided three PhD candidates as a primary supervisor and is supervising four more. His doctoral thesis was a monograph.

Impact of the Pandemic on PhD Candidates: Henrik noted that the pandemic affected his PhD candidates differently based on the nature of their research. Those engaged in classroom interventions faced significant challenges due to pandemic-related restrictions, particularly in accessing schools and conducting fieldwork. Conversely, candidates focused on desk-based research, such as literature reviews, experienced fewer disruptions. One of his candidates, involved in empirical research, had to receive an eight-month extension due to difficulties in data collection, exacerbated by strikes in the secondary education sector.

Changes in Supervision Practices: The transition to online supervision did not significantly affect Henrik, as he was already accustomed to conducting supervision via video conferencing tools like Teams and Zoom. However, he missed the informal, face-to-face interactions that often enrich the supervisory relationship. He noted that the absence of casual corridor conversations led to a more formal and structured online interaction.

Home Office Experience: Henrik found the exclusive home office setup challenging and detrimental to his well-being. He prefers a balance between working at the office and from home. The lack of physical interaction with colleagues and the continuous remote work environment negatively impacted his mental health, requiring him to seek professional health support.

Workload Changes During the Pandemic: Henrik reported that his workload related to PhD supervision did not increase significantly during the pandemic. However, other responsibilities became more demanding, and the overall context of working from home without the usual workplace interactions made certain tasks more difficult.

Support from Employer: There was no specific support provided by his employer concerning his role as a PhD supervisor during the pandemic. Support efforts were more generalized and not tailored to the unique challenges faced by supervisors.

Concerns for PhD Candidates: Henrik was particularly concerned about the mental health of his candidates, noting that the isolation and disruption caused by the pandemic were significant stressors. He proactively discussed these issues with his candidates, acknowledging the challenges faced by those with families and those who were isolated without a support network.

Personal Health Concerns: The pandemic had a substantial impact on Henrik’s mental health, highlighting the importance of considering the well-being of supervisors along with their candidates during such crises.

Effect on Completion Times: Henrik observed that the pandemic inevitably led to delays in the completion times of his PhD candidates, with some requiring extensions. He noted a disparity in how extensions were granted, suggesting a need for more consistent criteria.

Preparation for Article-Based Theses: Henrik believes that most PhD candidates are not well-prepared to write article-based theses, as their previous academic training typically does not include writing journal articles. He spends significant time discussing the publication process with his candidates to demystify it and help them understand the expectations of journal editors and peer reviewers.

Overall Reflection: Henrik’s experience reflects the diverse impacts of the pandemic on different types of research activities and highlights the importance of flexibility and support in PhD supervision. His proactive approach to discussing mental health and the structural changes in supervision practices illustrate adaptive strategies that can be beneficial in navigating future disruptions in academic settings.

3.2.5 Analyzing the interview with Luna

Introduction: Luna, aged 55, specializes in English as an Additional Language didactics. She completed her doctoral degree with an article-based thesis and has supervised a total of 11 PhD candidates, two of whom have completed their dissertations under her primary supervision.

Impact of the Pandemic on PhD Candidates : Luna discussed the varying impacts of the pandemic on her supervisees. One candidate, who was already far along in her research when the pandemic hit, was less affected in terms of supervision but faced uncertainty and stress related to her digital dissertation defense using Zoom. For two new candidates who started during the pandemic, the experience was particularly challenging. They struggled with integrating into the academic community and adapting to remote work, significantly affecting their progress and emotional well-being.

Changes in Supervision Practices : The pandemic required Luna to adapt her supervision methods, emphasizing digital communication tools and frequent check-ins via Teams, Zoom, or phone apps. She noted that these changes allowed for maintaining close communication but shifted many supervision interactions to support coping with the emotional and logistical challenges posed by the pandemic.

Home Office Experience: Luna had a positive experience working from home, which was facilitated by having enough space and a family structure that supported a conducive work environment. She did not face significant challenges balancing work and family life, which helped maintain her productivity and well-being.

Workload Changes During the Pandemic: While her direct supervision workload remained stable, Luna’s role as a researcher education coordinator significantly increased her overall responsibilities. She was deeply involved in supporting a broader range of PhD candidates beyond her direct supervisees, which included mediating between candidates and their supervisors and helping navigate the challenges posed by the pandemic.

Support from Employer: Luna felt well-supported by her employer, particularly in terms of responsiveness to her needs and concerns as she navigated her roles during the pandemic. This support was crucial in managing the increased demands on her time and ensuring the well-being of the candidates for whom she was responsible.

Concerns for PhD Candidates: Luna expressed significant concern for the mental well-being of her candidates, noting that the pandemic exacerbated feelings of isolation and stress. She was particularly worried about those who could not integrate into the academic community or faced severe disruptions in their personal lives.

Personal Health Concerns: Despite managing her workload and maintaining her health, Luna acknowledged the intense pressures of her role during the pandemic, which were compounded by the high demands of her coordinator position.

Effect on Completion Times: Luna observed that the pandemic delayed completion times for many PhD candidates, with extensions being necessary but variably granted. She emphasized the importance of transparent and equitable handling of extension requests to ensure fairness.

Preparation for Article-Based Theses: Luna believes that PhD candidates are generally underprepared for writing article-based theses, attributing this to the educational focus on monographic rather than article-based work before the PhD level. She highlighted the importance of guidance in academic writing and understanding publication processes as essential components of PhD education.

Overall Reflection: Luna’s experience during the pandemic underscores the critical role of adaptability in supervision, the importance of mental health support for PhD candidates, and the need for clear communication and guidelines in managing extended impacts on doctoral education. Her proactive approach to addressing these challenges reflects a comprehensive and empathetic supervision style aimed at supporting candidates through unprecedented times.

3.2.6 Analyzing the interview with Lydia

Introduction: Lydia, aged 52, specializes in educational research, focusing on professional development, assessment, and teacher education. She completed her doctoral degree through a monographic thesis and has supervised three PhD candidates to completion, with six currently under her guidance.

Impact of the Pandemic on PhD Candidates: Lydia noted that the pandemic affected the progress of her PhD candidates, especially those with young children or those who started their projects around the onset of the pandemic. The challenges of remote work and caring for family members led to minor delays in their research timelines.

Changes in Supervision Practices: For candidates who had already started their projects, Lydia managed to continue effective supervision by meeting them on campus when possible. However, starting a supervisory relationship entirely online via Zoom or Teams with new candidates presented difficulties, particularly in building rapport and trust.

Home Office Experience: Lydia found working from home to be somewhat liberating and enjoyed the quiet environment, which contrasted with the often-hectic campus life. Her home setup, which included adult family members who managed their responsibilities independently, provided a conducive environment for work without significant distractions.

Workload Changes During the Pandemic: While the actual supervision tasks did not significantly increase in time, Lydia spent more effort on providing emotional support to her candidates. Discussions often veered from academic topics to personal well-being, reflecting the heightened anxieties and social isolation experienced by the candidates.

Support from Employer : Lydia expressed disappointment with her institution’s lack of direct support during the pandemic. The focus remained on expecting faculty to adapt and manage without specific interventions aimed at easing the transition to remote supervision or addressing the unique challenges posed by the pandemic.

Concerns for PhD Candidates: She was particularly concerned about the psychological well-being of her candidates, as many were navigating difficult life stages compounded by the pandemic. Lydia felt a strong responsibility to reassure them and help them maintain confidence in their ability to progress in their research.

Personal Health Concerns: Lydia did not report significant concerns about her own health, feeling relatively privileged and well-adapted to the circumstances. She maintained a positive outlook, supported by stable family dynamics and the ability to engage in outdoor activities, which helped preserve her mental well-being.

Effect on Completion Times: Acknowledging the inevitable delays caused by the pandemic, Lydia noted that extensions were likely necessary for most PhD candidates during this period. She appreciated that post-pandemic policies allowed for extensions to address disruptions, especially those related to family responsibilities.

Preparation for Article-Based Theses: Despite not having written a synopsis herself, Lydia observed that candidates often lack preparedness for writing article-based theses, a gap she attributes to the traditional focus on monographic work at earlier academic stages. She advocates for enhanced training and support for candidates transitioning to this format.

Overall Reflection: Lydia’s reflections reveal a nuanced understanding of the challenges faced by PhD candidates and supervisors during the pandemic. Her approach highlights the importance of flexibility, emotional support, and the need for institutions to provide clearer guidelines and more robust support systems to adapt to such unprecedented circumstances effectively. Her experience underscores the critical role of empathy and adaptability in academic leadership during crises.

3.2.7 Analyzing the interview with Michelle

Introduction: Michelle, 41, specializes in educational science, teacher education, and language didactics. She has previously supervised five PhD students to completion and is currently the main and co-supervisor for ten PhD candidates.

Impact of the Pandemic on PhD Candidates: Michelle reported varied impacts of the pandemic on her PhD candidates. Those who were in the final stages of their research before the pandemic began experienced minimal disruptions, benefiting from the shift to remote work which allowed them more focused time for writing. However, candidates in earlier stages of their projects or those with young children faced significant challenges due to reduced childcare hours and the need to juggle multiple responsibilities.

Changes in Supervision Practices: The pandemic greatly affected Michelle’s ability to provide regular supervision. With the demands of her own childcare responsibilities and the limitations of remote work, the frequency and quality of her interactions with her PhD candidates suffered. Supervision sessions were delayed, and Michelle had to adjust her practices, often conducting meetings via phone, online with Zoom or Teams, or in socially distanced outdoor settings.

Home Office Experience: Michelle found working from home to be extremely challenging, particularly due to the presence of young children and the constant interruptions that blurred the lines between work and home life. She experienced a persistent sense of being unable to adequately meet all her responsibilities as a supervisor and a parent.

Workload Changes During the Pandemic : Her workload related to PhD supervision became more demanding due to the difficulties in maintaining regular and effective communication. Michelle had to find creative ways to support her students, which often meant extended work hours and adapting to less conventional interaction methods.

Support from Employer: Michelle expressed significant disappointment with the lack of support from her employer during the pandemic. She felt that the institutions did not provide clear guidelines or additional support for managing the unique challenges brought on by the pandemic, leaving supervisors to manage as best they could under difficult circumstances.

Concerns for PhD Candidates: Michelle was particularly concerned about the psychological well-being of her candidates, noting that the isolation and disruptions affected different groups in varied ways. She observed that while parents were stressed and overextended, single young men often felt isolated and unproductive, which sometimes led to detrimental lifestyle changes.

Personal Health Concerns: Michelle mentioned that, like many in academia, she was accustomed to working excessively and did not have time to focus on her own health due to the demands of the pandemic situation.

Effect on Completion Times: Michelle anticipated that the pandemic would likely extend the completion times for many PhD candidates due to delays in data collection and the general disruption of academic schedules. She noted that while some extensions were granted, many were not, which added to the stress and uncertainty for the candidates.

Preparation for Article-Based Theses: Michelle believes that PhD candidates are generally not well-prepared to write article-based theses, which is often not addressed until during the PhD program itself. She emphasized the importance of structuring doctoral education to prepare better candidates for the realities of academic publishing and the peer review process.

Overall Reflection: Michelle’s experience during the pandemic highlights the complex challenges faced by PhD supervisors. Her insights underscore the need for better institutional support and clearer guidelines to navigate such unprecedented situations. Her commitment to adapting her supervisory practices despite personal and professional challenges demonstrates her dedication to her role and the success of her students.

3.2.8 Analyzing the interview with Ollie

Introduction: Ollie, aged 55, specializes in educational science and has completed his doctoral degree with a monograph. He has guided one PhD candidate to completion and is currently supervising three, with one about to defend their thesis.

Impact of the Pandemic on PhD Candidates: Ollie noted significant disruptions for his PhD candidates due to the pandemic. One candidate was fortunate to have completed major data collection just before lockdowns, which somewhat insulated their progress. However, others struggled as their research depended heavily on data collection in schools, which became nearly impossible due to access restrictions and subsequent strikes affecting the school system.

Changes in Supervision Practices: While the physical data collection was hindered, Ollie found digital supervision effective, especially for discussing and editing texts. He appreciated the direct focus on the text that digital platforms such as Teams or Zoom facilitated, contrasting with the sometimes-awkward setups of physical meetings. Nonetheless, the lack of access to schools for his candidates meant there was less content to supervise, which altered the dynamics of his guidance.

Home Office Experience: Ollie had a relatively positive experience working from home, appreciating the convenience and reduced commute time. He noted that being at home allowed for a more relaxed dress code and flexible work hours, although he acknowledged a potential for decreased social interaction and the blurring of work-life boundaries.

Workload Changes During the Pandemic: Ollie’s workload in terms of PhD supervision remained largely the same, but the nature of the supervision changed. He spent more time helping candidates pivot their projects to adapt to the new realities, which included more discussions and finding alternative approaches to research obstacles.

Support from Employer: Ollie felt that there was a lack of specific support for PhD supervisors from his employer during the pandemic. The focus seemed to be more on undergraduate and master’s students, with little attention paid to the challenges faced by PhD candidates and their supervisors.

Concerns for PhD Candidates: He was concerned about the delays and the psychological impact on his students, noting the challenges of maintaining motivation and morale under such uncertain and stressful conditions.

Personal Health Concerns: Ollie was proactive about maintaining his physical health during the pandemic, investing in ergonomic furniture to ensure comfort while working from home. He did not express concerns about his psychological health, suggesting a pragmatic approach to dealing with the pandemic’s challenges.

Effect on Completion Times: He anticipated that the pandemic would significantly delay his PhD candidates’ completion times, mainly due to disrupted data collection processes. Ollie stressed the importance of data quality and how difficulties in data collection could impact the overall quality of doctoral research and subsequent publication opportunities.

Overall Reflection: Ollie’s insights reflect a nuanced understanding of the diverse challenges posed by the pandemic to doctoral education. His adaptation to online supervision using videoconferencing platforms such as Zoom or Teams highlights the potential benefits of digital platforms for focused academic work, even as he recognizes the significant disruptions to traditional research pathways. His experience underscores the need for institutions to provide more robust support systems for doctoral candidates and supervisors, ensuring that doctoral training quality and integrity are maintained even in adverse circumstances.

3.2.9 Analyzing the interview with Tyler

Introduction: Tyler, aged 60, specializes in the philosophy of science, organization, and educational leadership. He completed his doctorate with a monograph and has guided two PhD candidates to completion, with four currently under his supervision.

Impact of the Pandemic on PhD Candidates: The pandemic significantly disrupted the plans of Tyler’s PhD candidates, particularly affecting those involved in international collaborations and empirical research. One candidate missed a crucial research stay in Italy, impacting their opportunity to engage with an international academic community. Another had to revise their empirical approach due to restricted access to schools, which was a common issue during the pandemic.

Changes in Supervision Practices: Tyler’s supervision was heavily affected by the pandemic, with all interactions moving to digital platforms, including Teams and Zoom. This shift resulted in less frequent and less personal guidance, which he felt was less effective than the planned intensive seminars abroad. Like Ollie, however, Tyler noted some benefits to digital supervision using videoconferencing platforms, such as the ability to engage with text during sessions directly.

Home Office Experience: Initially, Tyler took on additional teaching responsibilities to compensate for colleagues struggling with digital formats, which increased his workload. Over time, he found a rhythm of working from home and even appreciated the focused time that allowed him to complete a book. He alternated working from home and the office, leveraging the strengths of both environments to maintain productivity.

Workload Changes During the Pandemic: Tyler’s workload in terms of PhD supervision did not increase significantly. Digital Teams or Zoom meetings tended to be shorter and more focused, which somewhat compensated for the increased preparatory work required for effective digital instruction.

Support from Employer: Tyler expressed frustration with his institution’s management during the pandemic, particularly concerning doctoral courses and the increased bureaucratic oversight that he felt stifled academic freedom. He noted a lack of focus on the needs of PhD supervisors and candidates compared to other groups within the university.

Concerns for PhD Candidates: While not overly concerned about the mental and physical health of his candidates, Tyler was worried about the practical aspects of their research, especially those needing to conduct fieldwork, which was severely impacted by the pandemic restrictions.

Personal Health Concerns: Tyler did not express particular concerns about his health; however, he took proactive measures to ensure a comfortable working environment by investing in ergonomic office equipment.

Effect on Completion Times: Tyler anticipated that the pandemic would extend the completion times for his PhD candidates, especially due to disruptions in data collection and the broader impact on academic research activities.

Overall Reflection: Tyler’s experiences reflect the complex challenges faced by academic supervisors during the pandemic, balancing the shift to digital platforms with maintaining academic rigor and support for their candidates. His story highlights the need for institutions to provide better support and flexibility for supervisors and PhD candidates during crises, ensuring that academic standards and well-being are maintained. Tyler’s ability to adapt and find personal benefits during the pandemic, such as completing a book, also underscores the potential for finding opportunities in the face of challenges.

3.2.10 Comprehensive analysis of the Main findings across nine interviews of doctoral supervisors in Norway

3.2.10.1 overview.

This analysis integrates the findings from interviews with nine doctoral supervisors in Norway, structured by the interview guide (based on the main findings from the survey) and analyzed using Braun and Clarke’s (2021) approach to reflexive thematic analysis. The analysis focuses on how the COVID-19 pandemic affected the progression of PhD candidates and the corresponding changes in supervision practices.

Main Themes Identified:

1. Impact of the Pandemic on PhD Progression:

• Disruptions in Data Collection : Most supervisors reported significant disruptions in their candidates’ ability to collect data, especially those requiring access to external facilities like schools or international institutions. This was primarily due to lockdowns and restrictions imposed to curb the spread of the virus. As one supervisor noted: “One of my candidates had to delay their project significantly due to the inability to collect data as schools were not accessible.” (Ollie)

• Adaptations in Research Plans : Many candidates had to alter their research methodologies or adjust their empirical scopes to suit the new constraints, highlighting the flexibility required under crisis conditions. However, one of the supervisors mentioned that: “It affected them very differently. I had three candidates before the pandemic, and two of them were barely affected. However, the third struggled significantly with data collection due to difficulties in recruiting informants.” (Gabbie)

2. Changes in Supervision Practices:

• Shift to Digital Supervision : All supervisors transitioned to online platforms for conducting supervision, such as Zoom, Teams, or phone apps (e.g., Facebook Messenger, WhatsApp). While some found digital tools effective for sharing and reviewing written work, others felt the lack of physical presence reduced the quality of interaction and guidance they could provide. As one supervisor noted: “Digital supervision worked very well because it allowed sharing and discussing texts more effectively than in-person meetings. This actually enhanced the focus on the text during sessions” (Ollie).

• Increased Need for Emotional Support : Supervisors noted an increased need to support the psychological well-being of their candidates, as many struggled with isolation and stress due to the pandemic. As one supervisor noted: “I was particularly attentive to the mental health of my candidates, especially those without local family support. Regular check-ins were crucial during this period” (Gabbie).

3. Work Environment and Work-Life Balance:

• Home Office Challenges : Responses about working from home were mixed; some supervisors appreciated the flexibility and reduced commute times, while others struggled with distractions and the blending of personal and professional spaces. As one supervisor mentioned: “I actually enjoyed working from home as it provided a peaceful environment, but I missed the informal interactions with colleagues.” (Lydia)

• Institutional Support : There was a notable lack of targeted support for supervisors from their institutions. This often left supervisors and their candidates feeling overlooked in broader university responses to the pandemic. As one supervisor noted: “There was no specific support for me as a PhD supervisor during the pandemic. The general support was the same as for all staff members” (Lydia).

4. Professional Development and Academic Output:

• Delays in Academic Milestones : The pandemic delayed key academic milestones, including thesis submissions and defenses, primarily due to halted data collection and extended research timelines.

• Publication Challenges : The disruption also impacted candidates’ abilities to publish their research, a crucial component of their academic careers, due to delays and changes in their research projects.

Integration of Findings with Saldaña’s Coding Framework and Interview Guide:

• Using Saldaña’s coding method allowed for identifying recurring challenges and adaptations among the supervisors’ experiences. The thematic analysis revealed a consistent need for increased flexibility in research planning and supervision methods.

• The interview guide helped maintain a focus on how the pandemic specifically impacted various aspects of PhD supervision and candidate progression. It ensured that all relevant areas, such as changes in work routines, supervision adjustments, and overall impacts on PhD timelines, were systematically explored.

Comprehensive Assessment : The interviews collectively underscore the resilience and adaptability required by PhD candidates and their supervisors during the pandemic. They highlight several areas for improvement:

• Enhanced Institutional Support : Institutions clearly need to provide more structured support tailored to the needs of PhD candidates and supervisors during crises.

• Flexibility in Research and Supervision Plans : Adapting research plans and supervision methods to accommodate unexpected disruptions is crucial for maintaining the integrity and continuity of PhD education.

• Focus on Mental Health : The increased emotional and psychological support needed by candidates suggests that institutions should integrate mental health resources more fully into their doctoral training programs.

• Preparedness and Training : The experience has shown the importance of preparing PhD candidates for unexpected changes in their research environment, including training in digital tools and remote research methodologies.

In conclusion, the pandemic has not only disrupted traditional PhD education paths but also provided insights into how flexibility, digital preparedness, and institutional support can be enhanced to better prepare for future crises. These insights are vital for shaping resilient and adaptive academic environments that can withstand global challenges while supporting doctoral candidates’ academic and personal well-being.

From the analysis of the nine interviews, a few aspects stood out as particularly notable, offering deeper insights (expansion) into the unique challenges and responses within the context of PhD supervision during the pandemic:

1. Resilience and Innovation in Supervision:

• Some supervisors noted that despite the significant challenges, the shift to digital platforms allowed them to explore new forms of engagement with texts and supervision methods. For example, one supervisor highlighted the effectiveness of digital tools for collaborative work on documents, suggesting that these might even surpass traditional face-to-face interactions in certain aspects. This adaptation was a positive takeaway that some found surprising and worth integrating into their post-pandemic practices.

2. Diverse Impacts on Different Research Types:

• The differential impact of the pandemic on empirical versus theoretical research was striking. Supervisors of candidates who needed to conduct fieldwork, especially in schools or abroad, faced severe disruptions. As one supervisor noted: “We had to adjust research plans significantly, shifting to alternative data sources and methods where possible.” (Kyle). In contrast, those whose work was more theoretical or could be conducted remotely experienced fewer setbacks. This variance highlighted certain types of research vulnerability to external disruptions, which was a notable point of concern.

3. Underestimation of Emotional Challenges:

• Another well known, but still important aspect was the depth of emotional and psychological impacts on PhD candidates as noted by their supervisors. The extent to which these challenges affected the candidates’ productivity and well-being was significant and perhaps underappreciated by the institutions themselves. This underscores a critical area for future academic support systems to address more robustly.

4. Lack of Institutional Support:

• The widespread sentiment of insufficient institutional support was particularly striking. Several supervisors felt that there was a lack of targeted strategies to support PhD supervision during the pandemic. This lack of support was not just in terms of transitioning to online modes but also in addressing the specific needs of PhD candidates and their projects during such a disruptive period.

5. The Positive Impact of Forced Adaptation:

• Interestingly, some supervisors pointed out that the forced adaptation to new circumstances led to unexpected benefits, such as enhanced focus and productivity in certain cases, and even opportunities for personal and professional growth, such as writing a book or developing new teaching methods. These outcomes, while not universal, were surprising positives that emerged from a generally challenging time.

The sentiment analysis of the 9 interviews (see attachment 4 in the Supplementary file ) showed some individual variations, but that resilience and adaptability among doctoral supervisors during the pandemic were quite common. Supervisors recognized the challenges but overall maintained a positive and proactive stance, focusing on solutions and effective management of their supervisory roles. The objective nature of their responses indicates a practical approach to dealing with the pandemic’s impact, emphasizing the importance of communication, adaptation to remote supervision, and institutional support.

These insights not only highlight the varied experiences of PhD supervisors during the pandemic but also suggest areas for improvement in how institutions support doctoral education in times of crisis. The resilience and innovative approaches developed during this period could inform future policies and practices to better support PhD candidates and supervisors alike.

3.2.11 Integrated analysis: the main findings from the interviews and the open survey responses

To integrate and analyze the findings from the interviews (see attachment 1) and the 1,483 open survey responses (see attachment 2) from the survey among 293 doctoral supervisors, we can draw on several key themes and concerns that emerge consistently across these data sources. This approach will help us understand the broader implications of the insights gathered from different perspectives within the same study.

1. Adaptation to Digital Tools and Platforms:

• Interviews : The interviews highlighted how supervisors adapted to using digital tools for communication and supervision. This was generally seen as effective but lacking in certain qualitative aspects, particularly in building deeper relationships and managing more nuanced discussions.

• Open Survey Responses : The survey also reflected a reliance on digital tools, with many supervisors recognizing their utility in maintaining continuity. However, there was also an acknowledgment of the challenges in fully replicating face-to-face interactions.

2. Ethical and Practical Concerns with Digital Supervision:

• Interviews : Concerns were raised about the relational and ethical implications of the lack of physical presence and interaction, and the extensive use of digital tools in academic settings during the pandemic.

• Open Survey Responses : Similar concerns were noted, with supervisors emphasizing the importance of ensuring academic integrity and the genuine intellectual development of PhD candidates.

3. Impact of the Pandemic on Supervisory Practices:

• Interviews : The pandemic’s impact was a significant theme, affecting the logistical aspects of supervision and the mental well-being of both supervisors and their candidates.

• Open Survey Responses : Responses indicated varied impacts of the pandemic, with some supervisors noting increased stress and difficulty in maintaining research productivity and supervisory quality.

4. Institutional Support and Professional Development:

• Interviews : There was a noted lack of sufficient institutional support for adapting to new modes of supervision and research during the pandemic.

• Open Survey Responses : This theme was echoed in the survey responses, with mixed reports about the availability and effectiveness of continuing professional development (CPD) related to research supervision. Some respondents felt unsupported, particularly in navigating the challenges posed by remote supervision and digital tools.

5. Preparedness of PhD Candidates:

• Interviews : Discussions highlighted concerns about the varying levels of preparedness among PhD candidates, especially in writing the synopsis and adapting to new research methodologies that include digital tools and remote data collection.

• Open Survey Responses : Supervisors expressed a range of experiences regarding candidate preparedness. While some noted their candidates were well-equipped, others pointed out significant gaps, especially in writing the synopsis and article-based theses and handling the referee process, the timeline and complex research independently.

6. Valuation of Supervision:

• Interviews : Supervisors discussed feeling that their efforts were not adequately valued by institutions, with a need for greater recognition and support for their roles.

• Open Survey Responses : This sentiment was reinforced by survey data, where some supervisors felt that their contributions to doctoral training were undervalued by their institutions, particularly when compared to other academic duties.

7. Suggestions for Institutional Changes:

• Interviews : There were calls for institutions to adapt more proactively to the changing landscape of doctoral education, including better training for using digital tools and more robust support systems for both supervisors and candidates.

• Open Survey Responses : Supervisors suggested various improvements, such as more structured professional development opportunities, better guidelines for remote supervision, and enhanced support for mental health and well-being.

3.2.12 Summary

The integrated analysis across interviews and open survey responses suggests a complex landscape of doctoral supervision during and potentially beyond the pandemic era. Key themes highlight both challenges and potential areas for policy and practice enhancements:

• Digital Adaptation and Ethical Concerns : While digital tools have provided necessary solutions for continuity in supervision, they bring up ethical concerns that institutions need to address more thoroughly, particularly concerning academic integrity and the quality of student learning.

• Support and Development Needs : There is a clear need for institutions to offer more targeted support and development opportunities for supervisors, addressing both the technical aspects of digital supervision and the broader pedagogical skills required in a changing academic environment.

• Recognition and Valuation of Supervision : Supervisors feel that their work is not sufficiently valued, suggesting that institutions should reevaluate how they recognize and support supervisory roles within the academic career framework.

• Candidate Preparedness : There is variability in how prepared PhD candidates are for the demands of modern doctoral research, indicating the need for more robust preparatory programs and entry assessments.

• These insights call for a strategic reassessment of doctoral training programs, supervisory support mechanisms, and institutional policies to better align with the evolving needs of both supervisors and their candidates.

4 Limitations and future research

The present study provides in-depths insights into PhD supervision during the pandemic; however, the study also has several limitations apart from inherited limitations of self-reports and interview data. Firstly, the findings might be context-specific to the educational setting in Norway. The unique characteristics of the Norwegian educational system, cultural aspects, and institutional structures may not be entirely generalizable to other countries. However, the globalization of doctoral education, with increasing international collaborations, international publishing, and standardization of academic practices, might mitigate this issue to some extent, making the findings relevant beyond the Norwegian context. Secondly, the study lacks data on PhD supervisors’ experiences prior to the pandemic. This absence of baseline data means we cannot directly compare the pre-pandemic and pandemic periods. Nonetheless, the experiences reported in this study correspond well with prior research on academic supervision ( Pyhältö et al., 2012 , 2023 ; Löfström et al., 2024 ), indicating that the challenges and adaptations observed are not entirely unprecedented, even if intensified by the pandemic context.

Future research should aim to explore the long-lasting impacts of COVID-19 on doctoral education. It is necessary to investigate whether the changes observed in supervisory practices during the pandemic are fleeting or have led to a permanent shift in how supervision is approached. Specifically, studies should examine if new models of remote supervision, increased flexibility, and the use of digital tools will continue to be integrated into doctoral education post-pandemic, or if traditional methods will resume dominance. This is of special interest in cases where PhD supervisors and PhD candidates are located at different institutions. By addressing these questions, future research can contribute to a deeper understanding of the pandemic’s legacy on doctoral education.

5 Conclusion

In this article we examined the experiences of PhD supervisors in Norway during the pandemic to answer the research questions:

1. To what extent has the COVID-19 pandemic impeded the PhD supervisors’ frame factors on the micro- level, and how do they perceive this situation?

2. To what extent has the COVID-19 pandemic influenced PhD supervisors’ frame factors on the meso- level, and how do they perceive this situation?

We conducted a cumulative data collection process and analysis, where survey questions were based on previously collected field dialog data, online observation data, seminar evaluation data, and document analysis data. The qualitative interview guide questions were built upon previously collected quantitative data (survey), and the Supplementary data was based on previously collected quantitative data (survey) and qualitative interview data.

The coherence between qualitative and quantitative findings is mainly examined based on confirmation , expansion , or discordance in this article ( Fetters et al., 2013 ).

The findings from the explorative case study revealed that the PhD supervisors faced numerous challenges during the pandemic, both professionally and personally. They found digital supervision with their PhD fellows via platforms like Teams and Zoom to be convenient and efficient but occasionally lacking in quality. They also encountered difficulties in addressing the psychosocial aspects of their PhD candidates’ experiences and faced various research-related challenges with their PhD-candidates during the pandemic. For PhD supervisors who extensively worked from home over a long period, the situation created new conditions that affected their job performance. These altered conditions hindered their research capacity, their ability to follow up with their PhD candidates and their capacity to fulfill other job responsibilities. Although the PhD supervisors received support during the pandemic, it seems that the incremental measures provided were insufficient. The PhD regulations were established before the pandemic under normal conditions and for normal circumstances. However, it appears that no significant adjustments have been made to accommodate the extraordinary pandemic conditions, which have altered some aspects of their professional roles as academics and PhD supervisors. This was particularly critical for PhD supervisors with young children, especially female supervisors, who had to deal with lockdowns, social distancing, remote work, homeschooling, quarantine for themselves and their children, and COVID-19 illness, since the data showed that they seemed to have more home responsibilities than men during the pandemic. We also found that some supervisors thought that female PhDs’ (with own children) submission rates to scientific journals have been delayed as a consequence of COVID-19, considering that women seem to have more home responsibilities. In addition, the supervisors thought that female supervisors (with own children) submission rates to scientific journals have been delayed as a consequence of COVID-19, considering that female supervisors seem also to have more home responsibilities (e.g., for childcare, household etc.).

This slow-motion disaster lasted up to 20 months and can be perceived as an “external intervention” or a naturalistic experiment which was impossible to predict for universities and society. The case study results indicate that it is more important than ever to plan for the unforeseen in order to be better prepared for the next societal crisis. Therefore, it is important to be vigilant and understand the gap between the formulation, transformation, and realization arenas when it comes to the distinction between incremental, semi-structural changes and fundamental changes in PhD regulations and guidelines brought on by societal crises. Although some support from employers has been offered, the overall PhD guidelines, regulations, and supervision norms remained unchanged in the transformation arena (meso- level) during the pandemic. On a general level, this highlights the need for better crisis preparedness at the doctoral level in the years to come.

A common finding related to RQ1 and RQ2 and across the different data sources was that the COVID-19 pandemic has significantly impacted some of the PhD supervisors in different ways on both micro- and meso-levels, and some of them perceive this long-lasting pandemic challenging and difficult, while others have experienced this to a lesser degree. This reveals a confirmation across the quantitative and qualitative data in the study. Also, these findings mostly confirmed and expanded on the understanding of the impact of the pandemic on PhD candidates ( Krumsvik et al., 2022 ), with some minor discordance.

More specifically, the PhD supervisors in the study were somewhat satisfied with the educational quality regarding digital teaching but experienced various supervision, research-related and psycho-social challenges. Although some of the supervisors received support during the pandemic, it seems like the majority did not receive sufficient support and their workload increased significantly during the pandemic. This is due to the high complexity of frame factors that have changed the underlying premises for doctoral education during the pandemic, affecting both the PhD- supervision and the PhD candidates’ feasibility on several levels. The regulations for PhD scholarships and PhD regulations, implemented before the pandemic in 2018, were designed under normal educational and social conditions and may not fully address the challenges faced during the pandemic. Therefore, this study shows that to reduce this gap and strengthen the feasibility of the PhDs and the frame factors for PhD-supervision, the institutions must significantly enhance their preparedness to effectively manage demanding situations at both micro- and meso-levels, ensuring they are fully equipped to address future societal crises of a similar nature.

When it comes to RQ3 we find both confirmation, expansion, and discordance across the quantitative and qualitative data. We find confirmation across the quantitative and qualitative data when it comes to the variability in preparedness of PhD candidates for writing the article-based thesis. Article-based theses present unique challenges compared to traditional monograph-based dissertations, particularly in terms of integration and the breadth of skills required. One of the primary challenges with article-based theses is integrating articles that may cover slightly different aspects of a research topic into a coherent overall thesis. This integration is critical, it requires a high level of academic writing skills and ability to secure the coherence of the synopsis. Candidates often come into PhD programs with varying levels of experience in academic writing and publication. The survey and interviews, as well as Supplementary data , indicate that many candidates are not well-prepared for writing article-based theses, highlighting a need for more targeted training in academic writing and publishing early in the doctoral process. The need for robust supervisory support is acutely felt in guiding article-based theses, where candidates must navigate the complexities of publishing in peer-reviewed journals alongside synthesizing their research in the synopsis. This implies that PhD-candidates both are taking a doctoral degree in the Norwegian context and at the same time are publishing articles for the international research context, which can be challenging.

We find expansion when it comes to the need to have guidelines for the synopsis. Supervisors reported significant variation in the guidelines for the synopsis across institutions, both in the qualitative and quantitative part, which can lead to confusion and inconsistency in expectations for candidates and supervisors. Some respondents found these guidelines sufficient, while others find them unclear or obscure, complicating their task of effectively guiding PhD candidates. Clear, comprehensible guidelines are essential for ensuring that the synopsis effectively synthesizes the research in a manner that meets academic standards ( Wollenschläger et al., 2016 ).

And we find some discordance regarding variability in candidate preparedness where both strands of the data indicated a significant variability in how prepared PhD candidates are when they enroll in doctoral programs. Candidates’ preparedness often depends on their previous educational experiences, which can vary widely regarding exposure to research methods, academic writing, and critical thinking skills. The variability in preparedness suggests a need for more robust preparatory programs to equip all incoming doctoral candidates with the necessary skills and knowledge to succeed in their research endeavors. Implementing comprehensive entry assessments could help identify specific areas where candidates might need additional support, allowing programs to tailor preparatory courses or early doctoral training to address these gaps.

These findings collectively point to a need for doctoral programs to clarify guidelines, particularly for the synopsis in article-based theses, to enhance support for supervisory roles, and to develop preparatory programs that address the broad variability in candidate preparedness. This is also based on research on the need for rubrics ( Wollenschläger et al., 2016 ), which shows that transparency around requirements and guidelines is important for students learning. By tackling these issues, institutions can better prepare PhD candidates for the demands of modern doctoral research, ultimately leading to more consistent and successful outcomes in doctoral education. And despite that only 20 (8.3%) of the supervisors agreed or strongly agreed that they were supervising a PhD candidate who had considered quitting the PhD program during the pandemic, it is important to be vigilant around the (complex) reasons that causes this, since this is in many ways a drastic decision, first of all for the candidate themselves, but also for the supervisors, as well as for the society in general who has invested almost 5 million Norwegian kroner in each PhD-scholarship. Dropping out can partly be related to the observed findings that many PhD candidates were unprepared for the intricacies of article writing, including the lengthy processes of submission and peer review, attached to their educational background, which primarily focused on monographic work at the bachelor’s and master’s levels. This also implies that while PhD’s are perceived, assessed and evaluated as student/candidates when they are completing assignments in a doctoral program, there might be a quite new situation for them when they submit their articles to scientific journals with blind review, where they are evaluated as other researchers (and not only as students/candidates). Such findings (and similar findings) seem to go “under the radar” in doctoral programs in Norway and by taking into account such “tacit knowledge” we might be better prepared to bridge the formulation arena and realization arena within doctoral education in the years to come. This development also demands a vigilance within doctoral education of the importance of theory development within doctoral education since international research shows that doctoral supervision is under-theorized and lacks a solid knowledge base ( Halse and Malfroy, 2010 ; Halse, 2011 ) where also eclectic use of theories ( Dalland et al., 2023 ) can improve this area.

Author note

GPT-4o ( OpenAI, 2024 ) was employed in this article to translate interview findings to English after a general thematic analysis conducted in Norwegian and as one of several validity communities for the open survey responses. The GPT-4’s output was manually examined, edited, and reviewed by the authors. The sentiment analysis of the 9 interviews was done by the first author and by using the GPT-4o. Then it was carried out a validation of this sentiment analysis by SurveyMonkey ( SurveyMonkey, 2024 ), Claude ( Anthropic, 2024 ) and Gemini Advanced ( Google, 2024 ).

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary material , further inquiries can be directed to the corresponding author.

Author contributions

RK: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. FR: Conceptualization, Data curation, Formal analysis, Methodology, Validation, Writing – review & editing, Writing – original draft. ØSk: Conceptualization, Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing. LJ: Conceptualization, Data curation, Methodology, Validation, Writing – original draft, Writing – review & editing. SS: Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. ØSa: Data curation, Validation, Writing – original draft, Writing – review & editing. KH: Methodology, Validation, Writing – original draft, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

We would like to thank all doctoral supervisors for their responses to the surveys and for participating in interviews and focus groups on this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2024.1436521/full#supplementary-material

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Keywords: PhD-supervisors, experiences, COVID-19, supervision, PhD-fellows, frame factors

Citation: Krumsvik RJ, Røkenes FM, Skaar &O, Jones L, Solstad SH, Salhus & and Høydal KL (2024) PhD-supervisors experiences during and after the COVID-19 pandemic: a case study. Front. Educ . 9:1436521. doi: 10.3389/feduc.2024.1436521

Received: 22 May 2024; Accepted: 15 July 2024; Published: 09 August 2024.

Reviewed by:

Copyright © 2024 Krumsvik, Røkenes, Skaar, Jones, Solstad, Salhus and Høydal. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Rune J. Krumsvik, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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DU Professor Helps Solve Famous 70-Year-Old Math Problem

Jordyn reiland.

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Assistant Professor Mandi Schaeffer Fry is the first faculty member to be published in the Annals of Mathematics since the 1880s.

University of Kaiserslautern Professor Gunter Malle, University of Denver Assistant Professor Mandi Schaeffer Fry and University of Valencia Professor Gabriel Navarro pose for a photo after announcing their theorem in Oberwolfach, Germany.

Whether it be flying trapeze, participating in competitive weightlifting or solving math problems that have confounded academics for decades, Mandi Schaeffer Fry enjoys chasing the next adventure.

Schaeffer Fry, who joined the University of Denver’s Department of Mathematics in the fall of 2023, will be the first faculty member since the 1880s to be published in the Annals of Mathematics , widely seen as the industry’s most prestigious journal.

In 2022, Schaeffer Fry helped complete a problem that dates to 1955—mathematician Richard Brauer’s Height Zero Conjecture.

“Maybe one of the most challenging parts, other than the math itself, was the knowledge of the weight that this would have on the field,” Schaeffer Fry says. “If you’re going to make an announcement like this, you have to be darn sure that it’s absolutely correct.”

Over the years, number crunchers have worked on the problem at universities across the globe, and some found partial solutions; however, the problem was not completed until now.

“Mandi’s accomplishment is exciting. Solving Brauer's Height Zero Conjecture is remarkable,” Mathematics Department Chair Alvaro Arias says. 

The work is also a testament to DU’s achievement as a Research 1 (R1) institution.

Fry and her collaborators—University of Kaiserslautern Professor Gunter Malle, University of Valencia Professor Gabriel Navarro and Rutgers University Professor Pham Huu Tiep—worked around the clock over the course of three months in eight-hour shifts during the summer of 2022 to find a solution.

In April, that work was accepted for publication in the Annals of Mathematics.

'Brauer's Height Zero Conjecture (BHZ) was the first conjecture leading to the part of my field studying 'local-global' problems in the representation theory of finite groups, which seek to relate properties of groups with properties of certain nice smaller subgroups, letting us 'zoom in' on the group using just a specific prime number and simplify things," Schaeffer Fry says. 

"The BHZ gives us a way to tell from the character table of a group (a table of data that encodes lots, but not all, information about the group) whether or not certain of these subgroups, called defect groups, have the commutativity property," she adds.

This paper was especially meaningful to Schaeffer Fry as she had always wanted to work with Malle, Tiep and Navarro as they have been her primary mentors. Tiep was her PhD advisor and this was the first time they had worked together since then.

Fry believes she has solidified her place in the field and knows she’ll likely never top this accomplishment, but she’s always looking for the next adventure—whether that’s in or out of the classroom.

Flying high and pumping iron

When Schaeffer Fry isn’t on DU’s campus working with students or conducting research, you can find her flying trapeze and competitive weightlifting.

Schaeffer Fry became involved in competitive weightlifting during graduate school, and, in the last year of her PhD at the University of Arizona, she defended her dissertation one day and got on a plane and competed at the national level for “university-aged” athletes—which included Olympians.

While she now lifts weights more casually, Schaeffer Fry competed last September in an over-35 competition and qualified for the USA Weightlifting Masters National Championships.

It was a “field trip” during a conference in Berkeley, California, in 2018 that led Fry to become enamored with flying trapeze.

In fact, she enjoyed it so much she signed up to be a member of Imperial Flyers, an amateur flying trapeze cooperative located in Westminster. Once she found out about the sport, her previous experience as a gymnast made it a natural fit.

Not only is she working on her own intermediate tricks, she’s also a “teaching assistant” at Fly Mile High, the state’s only flying trapeze and aerial fitness school.

“It’s exhilarating; it’s gotten me a bit over my fear of heights,” she says.

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