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Machine learning has connected with many other displines or research directions, for example it has been combined with materials, fluid mechanics and so many research directions, so what is your daily life if you have to use machine learning methods to complete your PhD topic or your postdoc research?

For example, you can give me a schedule or proportion or your daily life, for example, you have to use half of your workday to decode the ML literatures or algorithms and program, debug, run and test this model, and the other half to do experiments.

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    Also, the day in the life of a PhD student is probably quite different from a "researcher" in general.
    – Buffy
    Aug 14, 2021 at 12:05
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    This depends on a lot on the country, exact research subject, and knowledge that the PhD student may have in advance. Pretty much anything you can imagine is possible.
    – Louic
    Aug 14, 2021 at 12:49
  • Reading about the different project possibilities in the AIMLAC CDT (cdt-aimlac.org) might be helpful
    – Luismi98
    Aug 14, 2021 at 15:09
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    ML isn't my field, so I can't provide specific help. But expect to work hard, read a lot, and find some things confusing. Your description, however, is too "methodical". Finding problems to work on is a big part of any doctorate. Developing methodology. Carrying it out. The "Finding a good problem" can be very challenging.
    – Buffy
    Aug 14, 2021 at 16:07
  • I am glad that these senior members are way positive about this. I am probably too young to understand it, so I deleted my previous answers. To OP, the answer to your question depends on your topic. If it's more empirical (most of time, it is), you will spend most of your time on coding, e.g, tuning models and numbers, and relatively less time on learning new algorithms.
    – hpwww
    Aug 14, 2021 at 16:42

2 Answers 2

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I’m a bit puzzled by the negativity of the current answers. From what I know about ML applied to neuroscience (computational neuroscience), and at least some U.S. universities, this isn’t even close to being the case.

It is true that you might help reviewing papers as well as work on grants - both your own, and others to help your PI. At this stage of your career though, it won’t make up anywhere near as much time as postulated in other answers; helping your PI with their grants might not happen at all because it's more common for postdocs instead. Assuming you consider an academic career, it’s also useful training as especially grant writing will make up more of your time in later stages.

As to classes, if there even are any, they will only happen in your first - possibly second - year. Instead, you rotate through labs at the beginning to help find out what you’d like to do (you’re not necessarily locked into computational work upon entering, and might decide to do experimental work instead). If you work interdisciplinary, it’s important to be good not only on the computational side, but also understand the experimental work and related biology, so, again, in any case it’s time well-spent.

When you hit the research stage, if you’re lucky you can hop on a project your adviser has handy for you. If not, you’ll spend some time reading and trying to find your own niche, hopefully working closely with your boss.

When the research begins, you’ll cycle through talking to the experimental groups you work with to decide next steps, and setting up models and running them. Running your models will make up the vast majority of your time then. As needed, you research alternative approaches at the side. When it finally comes to writing your paper, you and your co-authors likely iterate through a fair number of rounds to get it done so everyone is happy.

If you’re the academic type, it’s a rather fascinating life.

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  • This is factually not correct, at least at my university. But it sounds good and academics love to defend their system so why not upvoted? Aug 14, 2021 at 18:35
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    @Fourierflux: If I read your profile and contributions correctly, you are in a machine learning/cs department. This is from a computational (ML) neuroscience perspective (OP asks about interdisciplinary work - other fields using ML), where it is in my experience commonly true in the U.S. Aug 14, 2021 at 18:38
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    @FourierFlux, I'm still curious. Did you enter ML simply because it was "hot" or did you have a genuine interest in it? There is, IMO, great danger in entering hot areas without knowing what you are getting into. Your comments about everything other than coursework being worthless seems like you are ill prepared. Am I wrong?
    – Buffy
    Aug 14, 2021 at 19:21
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    You're confused, I can read arxiv papers on my own, the only meaningful benefit I got out of the PhD program is free courses for things I want to learn about but don't care to independently study. Getting payed 2k/month to work on poorly designed projects you have no interest in and pretend anyone cares about what other people in the research group are doing isn't worth it. Aug 14, 2021 at 19:28
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    @FourierFlux, another possibility, of course is that you are another Will Hunting for whom most (math, CS, ML...) problems are trivial. Every once in a while I've come across students who have and will shine much brighter than I ever did.
    – Buffy
    Aug 14, 2021 at 19:44
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30% you spend working on a pointless poorly constructed project that the PI conned someone into giving them money for, 30% on classes, 30% on meetings nobody cares about or wants to join in and 10% on something you actually want to study.

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    Awfully cynical. If your own life is that bad, perhaps you should consider something else.
    – Buffy
    Aug 14, 2021 at 14:48
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    Already working on it, I think academia is trash. Aug 14, 2021 at 14:49
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    To the OP, here. This is an exceptionally negative view. Don't expect your life to (also) be a worst case.
    – Buffy
    Aug 14, 2021 at 14:52
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    If you have a different experience as a full time ML PhD student feel free to write it up :). Aug 14, 2021 at 16:24
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    @Buffy yeah it’s cynical, but are negative opinions not worth people knowing about? Should we pretend no one has negative experiences in academia?
    – Dan Romik
    Aug 14, 2021 at 19:10

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