Better late than never. Started my PhD in my mid thirties and I'm glad I did.

That said, I knew exactly what I wanted to work on (it's relatively niche) and have been fortunate enough to find an advisor willing to let me work in that area. If you're unsure, then it might make sense to work in industry for a while and later decide if you want to come back for a PhD.

i don't think you realise how young 27 is

PhD is a double edged sword. What’s your aim? If your aim is income, you rather not do a PhD. A PhD degree is for you to “learn to learn” (conduct independent research). It’s a skill that could be valuable if you think broadly (even beyond ML). All other pursuits might be suited to industry applied science positions.

so much this. Was not sure if OP is trolling.

This field isn’t like mathematics - in my math program my advisor was very concerned that I was older and wouldn’t be as productive since I was near 30z “Most great works are done before 30.” or so they said. Well I bounced to ML and that’s not the norm in this field. Hell, a lot of the work is done by industry so the entire value system is turned on its head. (For better or worse.)

28 is the median age of starting a Ph.D. in Germany, you are by no means old and being a few years "behind" (which I don't think you are if you learned anything during your 3yoe soft skills, etc.) a small fraction of Ph.D. students does not matter when you consider how long of a carrier you have in front of you.

I worked as a Data Scientist before starting my Ph.D. and was bored with it which is why I started mine. If you think doing a Ph.D. excites you and/or you think it will open up more exciting jobs in the future do it! Else you can have a fine career with your current credentials.

Not OP, but same age and very similar situation. I'm glad to hear this. My prior interest was physics and the age-situation is even worse there.

Thanks for the reassuring words. Maybe I'm looking at the other end of the spectrum to feel like an imposter.

I started my PhD when I was 34, I say go for it!

As someone who’s also 27 and been debating whether/not to start a PhD, this is reassuring to hear!

Most of the people I know started their PhD right after undergrad. The grad student I worked with during my undergrad was actually 26 when he graduated with his PhD.

That being said, CS and (& specifically ML) seems to have younger PhD students than other fields.

> Is it more advisable in long run to stay and get your PhD or just leave and join some ML role in industry that takes Masters guys and get some real world experience on how to use ML to generate business value?

Answering with a Harry Potter quote that helped me answer this very question.

"Play to your strengths."

"I haven’t got any," said Harry, before he could stop himself.

"Excuse me," growled Moody, "you’ve got strengths if I say you’ve got them. Think now. What are you best at?"

Post to /r/PhD. Lot more knowledge there that’s specific to your case

I also started in early 30's but I felt like an old man lol, most of the peers are much younger than me.

[deleted]

I would not worry about your age so much, and the maturity can even help you.

I started my Ph.D. at 26, and took a long time to get it (at 32.. 6 years), but it really paid off. I now have my dream job in research industry, and putting all my training into very good use!

You might be able to start a more theory oriented ML topic and switch later if you are not liking it. I switched supervisors and topics two years in.

Dude, can I DM you?

Sure

As someone who's 25 in a month and applying to MS programs (and not expecting to get in, not for Fall 23), I expect I'll be 27 or 28 by time I start a PhD.

This's been a huge source of insecurity for me, especially considering so many people I see and interact with in the field are younger than me and yet already 1-2 years ahead of me in the same trajectory.

Empirically, late 20s is still young, but it never feels like that when you're the one in your 20s.

This is great advice. First ask yourself why you want to do a PhD.

> Any suggestions on pros and cons of ML research in academia vs a ML industry job of a masters grad would be really helpful!

I am very opinionated on this, so you may want to weigh this accordingly.

PhD: I wish I could get my time back. I would be comfortably retired by now. 4 or 5 years industry experience working for a FAANG gives you a >USD250K/year advantage in the US (plus 4/5 years of being in a highly paid position). The only way you can compensate for this is if you get a PhD is a super relevant area that happens to be exactly what they are looking for (rare nowadays).

Theoretical ML: It's BS, literally. Pretty much everything being published is circlejerk by a small number of people playing with toy models and using plenty of math to hide the fact that they do not know what's going on. One interesting, recent observation is that as you scale models up, the specifics of the architecture no longer matter much and pretty much anything reasonable will work well enough. Which brings me to

Academia vs. industry: There are a few industries that do applied research at a scale that makes a difference. This is because (1) they have access to unlimited training data (2) they have access to nearly unlimited resources (3) they have urgent, real problems to solve (usually, advertising :-) ). What happens in academia (when they not partner with one of these companies) is laughable

>One interesting, recent observation is that as you scale models up, the specifics of the architecture no longer matter much and pretty much anything reasonable will work well enough

Could you share some more information on this - is it from a paper? Would like to read more

Play the long game. There’s plenty of people who don’t start on their career defining works until they are “old”. A particular example is Maziar Raissi. He graduated with a BS I applied math in 2008. As far as I can tell he probably didn’t touch ML until 2016 or so, and from 2017-2020 or so he published several seminal works on Physics Informed Neural Networks. I think he would have been around 30 years old in 2017. Granted he came from an applied math background which definitely was a part of his success, but he didn’t publish anything in ML until later in his career.

My teachers always told me to go work in the world instead of doing a Ph.D.--their advice made me choose not to do a Ph.D. I still attended grad school for another degree--not ML-related, and I think learning on the job is much more effective and a better way to support yourself. You want an apprenticeship of sorts, a relationship with someone who is already building and maintaining systems. Seek spiritual insight and decide what you want in your life.

I disagree. 27 is old to start a PhD and I think this comment is dismissive. Many people want to start a family in their 30s and buy a house. That be difficult if you are just starting your career.

I'm older and still might go back for another graduate degree.

I have applied to start a PhD this year and I'm 50.

It doesn't actually matter what you do your PhD unless you plan on staying in academia. Very little of it is going to be valuable to employers, but very few employers are going to care because the criteria for getting a job in industry in computing are (a) have a pulse (b) demonstrated ability to program a computer.

> Is it more advisable in long run to stay and get your PhD or just leave and join some ML role in industry that takes Masters guys and get some real world experience on how to use ML to generate business value ?

Maybe do both? Can you find an industrially-focussed research project?

Im 26 and starting my bachelor thesis in 2 weeks. Its all good, nothing positive will come out of worrying about it since you cant change where you at anyway. Try to be happy for the experiences you’ve had and make the best of your current position. We’re not even 30. Bless

> PhD: I wish I could get my time back. I would be comfortably retired by now. 4 or 5 years industry experience working for a FAANG gives you a >USD250K/year advantage in the US (plus 4/5 years of being in a highly paid position). The only way you can compensate for this is if you get a PhD is a super relevant area that happens to be exactly what they are looking for (rare nowadays).

maybe in the US, OP is in Germany which is a diff ballgame salarywise

Sorry, I don't think you know what you're talking about.

In Germany, it's quite common to start your PhD later and continue well into your 30s whilst starting a family etc. This is because a PhD is treated as a real job that pays reasonably well. Also because the education in Germany is quite substantial (5-6 years diplom, 3 years masters, 5-6 years PhD). In contrast to a country like UK, where you can start at 21 and be done by 24, I will always respect and rank a German PhD much much higher.

As a UK academic, having to supervise bratty kids with very little depth of knowledge has become the bane of my existence. I will always pick someone with a bit more experience if I can.

You might want to consider looking into a PhD in computational neuroscience. There’s quite a bit of overlap and it’s still much more research focused and less industry dominated. Over the past 4-5y the fields converge more & more. Lots of peers are meanwhile at DeepMind and Google brain. If that’s your career goal.

The German Diplom doesn’t exist anymore and a Masters degree is not the next step for someone who completed a Diplom since they are considered equal. Nowadays it’s 3 years for a Bachelor‘s Degree and 2 for a Master’s. PhDs are also usually 3 years+, although I have heard that most people need more than 3 years.

I don’t disagree with the rest of your comment tho.

My totally subjective view is that part of the definition of a PhD is gaining expertise in a very narrow field of study. I have had multiple people explain to me that the goal is to become the “world’s expert” on a really specific topic by the time you graduate. What that means is that you aren’t getting a PhD to gain generalist knowledge about machine learning, you’re going to learn to do research and to refine the way you tackle hard problems.

I guess what I’m really trying to say is that, unless you study some specific topic that is exactly what some company happens to be researching at the time you graduate, which may be unlikely, you are going to have to pick up a new (adjacent) topic within ML at some point. Even if you fall into the former category of lucky people, the thing you have a lot of expertise in might fall out of favor in ten years, or the company you work for decides to stop investing time in an approach about which you’re really knowledgeable, so you have to pivot to stay relevant/stay at the company. This is part of what you learn to do (in my experience and others’ that I’ve spoken with) in a PhD.

The other thing to consider is that it’s often easier to learn to be more applied given a theoretical background than the opposite.

Finally, it depends on what kind of job you want. Do you want to do research in industry? My advice is to not let the internet convince you that a MS is just as good as a PhD when it comes to getting such a job. In my experience, the people I’ve known who land research jobs in industry with less than a PhD are exceptional cases.

EDIT: I wanted to add that my personal theory is that there is only one really good reason to do a PhD, which is that you feel like you need to know more about your research area. It’s impossible to say whether a PhD will result in a monetary benefit or even a better job than you would’ve had. At the end of the day, you should do it because there’s something inside you that says you need to because the material benefits are not guaranteed.

Agree, the diplom is being phased out. Didn't realise the master's is 2 years now. Do they not do a thesis anymore?

It's very disheartening that a 3 year PhD has become the norm. Unless you're a prodigy, you barely scratch the surface before having to write up and leave.

Exactly, and don't mention that it's 40 years minimum until retirement from 27... That will break them.

I thought that when comparing master degree vs PhD the latter wins because it can ask for a better salary, or am I wrong?

Most people have commented on how a person of your age would be viewed in academia and in industry. I'd like to take a different perspective: what kind of thesis you would produce.

This is of course impossible to say for sure, but the following questions may be of relevance:

1. How has your master thesis been going, and how enthusiastic have you been working on it?
2. Have you already been suggested a topic for your thesis, and to what extent have you been able to influence this choice of topic?
3. Do you struggle with procrastination?
4. When you procrastinate, do you find yourself doing things that could perhaps be developed further into useful skills for the job market?

Once you get older 2-3 years in life is just a small delta/noise. I have seen so many students drop out of a successful masters with publications instead of continuing to do a PhD because they wanted to finally stop studying. A PhD is really useful even in industry -- its true that a masters will initially get you successful faster but after 10-15 years in the industry the PhD starts to matter for leadership positions.

I am 32, and i am in my final year of master's student. after this is done, I will probably start Ph.D. why am I in interested PhD in ML even though I know I am not that kinda smart enough compared to my peers who can finish their master's in 1 year only? I love studying and researching and I wanna be a researcher in this field. I think you should ask yourself why did you after Ph.D. at the very first place. Because I am afraid you cant finish your Ph.D. later and struggle with what kinda job or career you wanna pursue. If you want to engage in academic field, then taking PhD is a better choice. about ML industry for PhD, I think it depends on the career choice that would be opened whether you wanna work in Germany or outside.

Be ware of the length of the program, find out ahead of time average graduation time for advisors of interest; top schools tend to take longer to graduate!

> Most of the people I know started their PhD right after undergrad.

It depends also on the continent. US based PhD students tend to be younger on average when they start their PhD than PhD students in mainland Europe.

This because in Europe it is often legally required to have completed bachelors + masters before you can start a PhD.

I started my PhD in the Netherlands when I was 26. My experience comparing to other PhD students at the university and in the country this was pretty much an average age to start.

In the Netherlands a masters can be anything between 1 years to 3 years, depending on the field of study.

Typically, only in areas where PhD (ability to conduct independent research) skills are important. Otherwise, you might get a net lower value.

The salary gap isn't that different from master to phd, while the person with a master degree will have an additional five to six years headstart on accumulating tech salary wealth and career growth.

Do you feel happy while learning and research new stuff in ML area?

If yes, it is recommended to continue the phD. If not, go and get a job. Do the stuff you like, that will make you happy in the rest of your life

If the ability to conduct independent research is the most valuable skill for a PhD... A master graduate could close that gap by publishing several papers during his/her career? Maybe he/she can also become more valuable since the additional years in the job market (as /u/dvorakcoder said)

Yes of course. There are more than one ways to achieve an outcome. That said, the skills you want to learn from a PhD are best learnt from spending dedicated time in one. The quality will show. You can hack around it. However, very few people are able to sustain.

In any case, the question of - is this a relevant skill to earn more income - will always be a personal choice and question.

I started my PhD when I was 27 (and finished when I was 34 FWIW), and now work in a Big Tech AI lab. Age-wise you're definitely still fine.

Hi, Thanks for taking out time to give actionable advise.

1. My masters thesis is going okay, I have made sufficient progress to graduate but I am on my own for my masters thesis. I felt that had I gotten support I would be able to get a paper out of it. For context I do my masters and also thesis at TU Munich Germany. The prospective PhD advisor was impressed with the topic of my MS thesis (very likely reason for him to extend me a position).
2. Yes, I was suggested a topic for my PhD program. There is some room to choose a topic within the broad area but the PhD advisor has recently moved from another institution to current one after graduating everyone there. So I am his only PhD recruit and he is hiring a post doc on a very well defined topic in game theory for ML robustness. The prof subtly hinted me to choose same topic.
3. I do struggle with procrastinating but since last years I have gotten better. However, the final results are not that impressive. I will explain that in context of my masters thesis. I have tried to be on my toes with my thesis but I still feel it is going on a very snail pace because I often get stuck and there is no obvious solution to my to-do tasks. I know that my advisors don't have ready made answers but I felt that any brainstorming on ideas would have helped me move faster and produce work that I would be legit proud of.
4. Yes, in times of procrastination I often watch tutorials on more practical aspects in CS like system design and software engineering. I believe I can also build a structure to learn these skills when I get stuck in my research.

Masters in top ranked schools in Germany (my personal experience from TU Munich a top German school) in a stem course is very rigorous, students have to take multiple independent research projects to graduate. I'm taking 5 semesters to graduate in CS. Average time to graduate in my program is 6 semesters. However the minimum time you can graduate in is 4 semester (a lot of times very challenging and a rushed way to compete it).

So you're right when you said that MS in Germany takes 3 years to get done with in reality. Although the official time to do it is 2 years.

I am not surprised by the limited amount of mentoring for your master thesis. I know that doctoral students are supposed to be very independent in Germany in comparison to many other countries, at least in more traditional academic disciplines. Maybe it is a little different in ML. Anyway, there seems to be the prospect of some brainstorming with the post doc, and there will be other PhD students, even if they don't have the same advisor as you. So, chances are that you would enjoy your PhD years more than you have been doing working on your master thesis.

Since you mentioned that you may drift towards more practical aspects of CS when you get stuck, you should also consider how much you enjoy programming. Can you easily sit for hours with your own hobby programming projects, almost not noticing how time passes? If this is the case, you would perhaps be more happy working on implementing ML techniques in industry than analyzing the theoretical aspects of it.

1. I agree with you from financials perspective. I see my peers having joined SWE posts in faang straight after bachelor's have been promoted to SDE2 and equivalent and live a very comfortable life.

2. I somewhat agree, my thesis also on very well defined properties of robustness in ML models but no explanation for larger models. We give theory for toy 2-3 layer networks with activations on hidden layers. However, recent progress in neural tangent kernels to explain DL is real.

3. I understand the faang argument. I also had a soft offer to join Amazon as a Applied Scientist I with just MS upon my graduation. However, that ship sailed due to Alexa hiring freeze. My prospective PhD program is completely rooted in academia. Maybe I can do some internship but my program doesn't have a industry collaboration.

1. General advice - if you don't have a very specific reason for doing a PhD e.g. to do a post-doc or stay in academia or really want to dig deep into a topic due to personal reasons/drive - I would rather not start a PhD.

2. If you don't want a 'meh' PhD you must either be really good and find a hands off supervisor or find a really good supervisor that is willing to work with you.

3. You can work in the industry on ML topics with just a master's, you just have to show that you can get things done.

Depends on the setup of the university. Mine had an 7 semester bachelor (5 semesters courses + mandatory internship + thesis) and a 3 semester master (2 semesters courses + thesis). Others have a 6/4 split but most seem to shoot for 10 semesters combined.

Three years for a PhD is really awfully short. I'm now 2,5 years in and if I'd stop now to write everything down it would look terribly half-assed.

I also went into my PhD after 30, although it was quite a few years ago. So the first part of your question is age, and in many ways, age is irrelevant, but in some surprising ways it's not. No one one will care when you did your PHd, and in many ways, mature students are better prepared for the type of 'unsupervised' work ethic that you need to succeed post masters. If you want an academic career, then age can have some hidden issues. For example, your papers will get referenced more the longer they are in print. Mature academics simply don't have the same amount of time to show that build up as academics that started younger. There also used to be special grants and positions for 'early career' academics, but many (not all) have dropped the max age requirements recently.

But maybe more important for you is the fact that you seem focused on applied research. You can 100% do that now with a masters and there are great career paths already open. If you are uncertain about the committment, it may not be the best choice. There is an old saying that you need to love your PhD topic at the start because you will hate it by the end.

One final qualifier though. 'Degree creep' means that many high end applied jobs are now asking for PhD simply because they know they can get it. I can't predict what the job market will look like in 10 years, but the number of candidates with PhDs looking for inductry work is certainly growing every year. Masters students may find themselves pushed down the workforce.

> What happens in academia (when they not partner with one of these companies) is laughable

Then again, what happens in industry (if there isn't at least a medium term path to financial gain) is non-existent. And some of us think there just might be other worthwhile things for humanity besides money.

I guess there's no "correct" answer and I'm a contrarian who doesn't believe in degrees (but in learning).

However, based on what you say and how you say it I get the impression that you are a "can do" person who isn't too keen on spending more time in a classroom.

Make of this what you might - and, since I don't know you, I might be totally wrong. Except for one thing that caught my eye: reinventing yourself! That is the single most important skill you can have (I've done it a dozen times and been quite successful). Being petrified in a profession (or discipline) seems awfully unattractive and the source of many ills.

But that's just me! So please don't take this as advice. It's just food for thought and I understand how difficult that decision must be!

So I think you've answered your own question. Starting a PhD at 27 in Germany is quite normal. If you're comparing yourself against UK/US graduates, all I can say is that people who make the hiring decisions definitely know and appreciate the extra experience that brings.

I'm doing my PhD in DL for predictive maintenance at TU Berlin. Struggled a lot with if what I am doing has any value. Started when I was 25. Not exactly the same but if you want to chat just DM me.

Note that if you want to land a job in industry working on theory, then keep in mind that there are not many such places. Even at Google, Meta, MSR I believe that theoretical researchers are only a small fraction of the total. Moreover, the competition is very fierce. You would likely compete with students coming from top US universities, which usually have very good connections to those places (because well most of them are in the US). Also, those students have a lot of publications since PhDs in US last more years. Hence, if you embark in this journey make sure that your supervisor has good connections to those places and try really hard to get internships there (again, this will be easier if your supervisor knows people in industry).

This. A PhD can ask for a higher salary than a master student, but a person with a master and 5 years of experience can ask for a higher salary than a PhD student.

I wonder if having a PhD will allow faster growth to a senior position such that a PhD will win out in the long term. I'm guessing on average the difference is not that big in the long term.

As others have said: ask yourself why you want to do a PhD. If it is to get a higher salary, you might want to think again.

> Theoretical ML: It's BS, literally

You say this, but I would argue most of the best people in the ML industry that I have personally witness posses a strongly rich theoretical background. Of course to answer very practical questions, I wouldnt maybe go to a theory book, or look into reading a texbook on algebraic geometry..... But the minds of those well-versed in theory tend to better understand novel situations and problems very quickly, and have a very adaptable mind.

So If a theoretician can correctly transition their "post-PHD" personalities to industry, I think they stand the best chance to be one of the most valuable team players, because for example "everyone" can code, or so they say, but not everyone can understand models in the depth of level as a theoretician.

For example if something isn't working, I would rather first seek the counsel of someone with a theoretical background working in industry, rather than someone who has only ever worked in industry, unless that person is exceptionally talented, and has something like 10 years of experience.

The advice I was given was that you should do a PhD only if research is the only thing you really, really want to work with for the rest of your life. 4-5 years of pain, stress, late nights, loneliness, and suffering at that level is ridiculous if you're only motivated by a better industry title or a higher salary.

Yep. I only just went back to school for my bachelor's at that age. I'm now 4 years in industry after graduation. Took me 6 years to complete my bachelor's.

OP is in a good position if they are working on a PhD. Also, for me, being older than my classmates was a boon for me.

I’d mostly agree. I would like to add one additional point. If you can, a PhD can change the way one thinks. The idea of formulation of a question, creating a plan to test it, evaluation criteria to monitor and drawing conclusions is beneficial even beyond research. However, those benefits only materialise if one views it from that lens. You will think differently from your peers which can give you an edge in sports, management etc. The question then arises is 1) Is this something one desires? and 2) what effort are you willing to spend to achieve this desire? That’s a personal choice to make.

I'm 32 and just got into it last year...

There is a perceived market need for PhDs in ML. It's in all the job descriptions. It will allow easier access to research oriented positions in industry.

Just some additional information: PhDs in Germany are meant to last 3 years, after that you have to fight for extensions. So even if 27 was old (not by a long shot, spring chicken) OP doesn't look at the 5-10 years of a US PhD. That's the benefit of being required to have completed a Masters. You enter straight into independent research, there's rarely any compulsory coursework/exams.

I started an MSc program at 27 (Information Systems, not ML), and was far from the oldest there.

Never let preconceptions about the "right" way of doing things prevent you from finding your own path. You're never too late.

You will learn tons of prictical relevance skills during your PhD. The theory behind is what later on lands you top jobs, after getting hired in the first place.

The real question regarding PhD in Germany is, though, how you are funded and what that means to your every day work. The strufgle gets real when PhDs doing company work or full time lecturing instead of research. Keep an eye out that you are not solely the underpaid overskilled.

Hey, I believe that is not at all a problem since I am joining a new but well funded federal research institute (CISPA). It is part of Helmholtz, therefore they offer me 100 percent contract with no bells and whistles of industry relationship. Therefore I'm not really bothering about the under compensation for work I would do as a PhD had I joined a industrial PhD program.

My issue was not the undercompensation: computer science and engineering are luxuriously funded with 100% in comparison to other research fields.

The question always is what you have to do for that 100%. If it’s really fully public funded and your professor does not expect corporate consulting/research projects from you, then consider yourself very lucky and take that chance!

Project work can be very fulfilling along research when you can apply your problem solutions. You must be insanely careful if these topics do not overlap since it drains your time and you would be compensated better with less stress in an industry job.

Though, it sounds very good, in your case :-)

Would you actually be retired tho? 250k for 4 years is 1m and in 10 you got 2.5m (of course you will get taxed to hell) cutting you down to ~~1m. Sure its good money but not enough to retire on.

Everyone else has addressed starting your PhD at 27, and they better be right, as I likely won't be starting my own PhD for another few years.

But, regarding the value of pure ML theory research e.g. convergence bounds, versus practical ML research e.g. quantization methods, my personal feeling for quite some time has been that purely theoretical ML research has been predominantly bunk. Machine Learning is so high dimensional that things that can't be proven universal can be nearly guaranteed probabilistically, and things that can be shown to be possible can be staggeringly unlikely; for example, just because the No Free Lunch theorem exists, doesn't mean that Adam won't work in the vast, vast majority of cases.

Someone with a PhD in pure ML theory, if they're good, is probably still perfectly capable of heading to industry and making bank, whether that's continuing to do ML theory research, moving over to applications, or just becoming a quant or something. But honestly? I just find screwing around with training models and shit to be way more fun, and you should try it, if you haven't already.

Hey, thanks for the inputs. Actually I did very empirical stuff for a while. Infact, I published in some computer vision venues too, eccv, cvpr workshop et al. My main issue with total empirical stuff was that I never knew why it didn't work? Did I do it correctly or was it just setup for failure. The only way was to just brute force all possible cases of implementation to hope if it improves numbers. And then, does the model with better leaderboard number actually do better in wild? It felt more alchemy than science. That doesn't discount the fact that empirical ML helps run lot of businesses and generates value, but that isn't a metric to call it science, right? This made me explore stuff which is more rigorous and probably works with (optimistic/loose) guarantee? I actually love applied DL for its potential but I would want it to be more methodical.

I think that's a fair criticism of applied ML as a field. I've definitely described Deep Learning as alchemy to friends.

For my work, the people who are paying for the models have a... sizable interest in confirming that the models will actually work in the field, so on occasion I've been called on to modify classical methods to fit, rather than just throwing neural networks at everything. Maybe you would like that kind of thing? Or, otherwise, there are a lot of people going after interpretability and robustness, and some interesting progress has been made.

Yeah I am actually Interested in classic stuff implementation and deployment from industry POV. I think doing work with classical ML models in industry is better depending upon ML maturity of clients. If I were a business with critical infra and ungergoing a digital transformation I would also be scared of DL stuff.

Btw thanks for the pointer on interpretability and robustness, I actually planned to work in robustness as part of PHD if I eventually join it.

Maybe you should check out some of the labs at the ETH Zurich? Yeah, you'd have to put up with Schweizzerdeutsch for three years, but it seems like they're doing some interesting work in the area.

Thanks :D I applied to ETH for this admission cycle, it's competitive so let's see what happens. Actually the Saarland advisor kind of circled me into accepting the offer. The offer was rolled out before I could see outcome of other applications 😅

My tip: Don’t call yourself “old” at 27 to your grad school colleagues because they will believe you. Especially if they are younger on average.

I'm planning on applying to the ETH once I finish my MS, mostly because I think the whole "ask a professor to hire you" schtick might be easier than getting in somewhere with a more formal application, given my great work experience and fucking dogshit undergraduate performance. Also, it's a three year program with no coursework and an actually decent stipend, compared to US programs that might average five years and pay barely enough to eat or pay rent.

In my program, in the US, the majority had a masters degree and 27 would be a normal age to start, but there is a lot of variance.

If I remember correctly, the total time of Bachelor + Masters is supposed to be 5 years, but the split can vary. 3+2 seems typical

Fwiw, Germany has a portion of people who stay enrolled forever because it doesn't cost anything and they may have a somewhat decent job on the side that funds them. That's not the kind of person who pursues a PhD, so I wouldn't put too much stock in averages here.

I disagree mostly. One should do a PhD if research is what one wants to do right now/for the next few years. The PhD is a unique opportunity to do focused, very deep research at the boundaries of human knowledge. You will most likely not be able to do that at any point later in your career (maybe still somewhat as a post doc, but certainly not as a professor, because then you are a science manager, and also not in industry). Also, the feeling of inventing the future when you make a discovery for the first time is just amazing.

Also, a PhD provides you with unique training to tackle unstructured problems, and having a PhD is often prerequisite to research positions in industry

However, I agree with the statement that one should not do a PhD if salary/prestige is what one wants. You have to be 150% self motivated in a PhD, otherwise it’s a recipe for misery.

Top research labs in Germany will hesitate taking someone that old, and pretty much age-discriminate older students.

Don't do a PhD to increase your employability, that's just bad.

When you ask for real impact, what do you actually want? People adopting your ideas? Your ideas moving the field forward? These are very different things.

Well the department here at TUM has hard deadline of graduation in 7 semesters. And both mean and median graduation time is 6 semesters. I agree that students take on part time jobs as working students in big firms that fund them, but they don't exceed 20 hours. My analysis is that there is lot of uncertain components that you have to navigate to get your degree (independent research credits, thesis), where what constitutes as sufficient work is subjective. If it were only coursework I would also look carefully at a student who took longer time to graduate :)

Well asking a prof to hire you is the conventional way but both ETH and EPFL, along with MPI (IMPRS programs) are moving to US style of admission cycle of once a year. Especially for AI related stuff. I'm not sure about how much the culture of open hiring from a prof will continue, until you graduate. So keep an eye on admission cycle in December and plan graduation accordingly. Even good profs in other Europe academia are being on onboarded to ELLIS (a Europe wide US style admission call for AI PhD programs). You would want to check that too! whenever you apply!

> One should do a PhD if research is what one wants to do right now/for the next few years.

I like how you framed this, but it's important to consider opportunity costs. In many fields, the PhD track is severely underpaid compared to industry. You'll spend a few years working more or less alone, and in a single organization. The longer you stay your PhD, the greater the cost of not finishing. I dropped out after 1.5 years at a PhD, and this was one of the reasons.

Fresh PhDs going into industry are also junior in many aspects. I've been working alongside one who recently finished a PhD on the topic we're working on in my team, and they do require training in many aspects like someone fresh from a master's. He excels in other areas, naturally, and that's why we hired them :)

Edit: The advice I was given is a little bit black and white to be honest. I think points from both sides are valid, but the importance of really, really wanting to do a PhD can not be overstated.

[deleted]

Do what your gut tells you. No one has a clue about how to live your life. If they think they do, they’re wrong. Having a PhD, especially in ML should be a godsend for you, since in 5 years people with these skills are going to be very VERY sought after

Not just with that, but take a look at levels.fyi and check how much a 1-level advantage at Google or Meta gives you as you progress (the 250K/year I mentioned is just comparing MS + 4Yrs to PhD)

Sure, that's why you can spend the rest of your life working on enumerative combinatorics (which, BTW, I just love). Just don't fool yourself thinking you are making the world a better place with a bunch of papers.

I am not disputing the morality of this, just stating what I see/know having worked in both academia and industry.

I'm a 28 year old who got an undergraduate degree in physics. Self-taught ML for a few years then got an industry job. Now I work at a materials science start up and make excellent money as an ML Research Lead while doing work I am interested in and passionate about.

I specifically chose to skip the masters/phd so that I could learn while getting paid rather than going further into debt. I'm also having a real time impact on really cool global challenges (we work on carbon reduction to get CO2 out of the atmosphere).

You gotta be ready to keep a good attitude after the 500th rejection tho....

Its def not a troll, it feels old when you are surrounded by 21 and 22 year olds

There is only one good reason to get a PhD: because you want to further our understanding of the field.

So do you want to further our understanding of the field, or do you want to build cool things and make lots of money?

I'm not saying you can't build cool things and make lots of money with a PhD, but I AM saying it's probably not the optimal path for someone with those goals to take. Your lifetime earnings will likely be higher if you just do ML engineering rather than ML research, and you'd spend more time building useful things that way too.

Jump ten years ahead and compare 31, 32 to 37. It is not as weird as it might seem to be.

Dm'ed you

ML Research in Academia
Pros
- Depth of technical understanding
- Opportunity to make a novel contribution
- Long-term more credibility and opportunities
- Research contacts
- Key scientifc skills: critical thinking, independent working, etc.
Cons
- Poor/very poor work-life balance
- Short to medium term financial sacrifice
- Academic culture is not for everyone

ML Masters + Industry Job
Pros
- Some technical understanding
- Key software skills
- Real world product delivery
- Finances
- Someone will care about what you're doing
Cons
- Projects can be a bit boring
- Just get something to work, usually cut-down approaches from academia
- Little theoretical understanding

Source: ML PhD graduate transitioned to industry

Generally I would say that if you really care about the underlying theory of ML then do a PhD, but be sure to consolidate that with coding experience in actual projects. You will only have trouble with jobs if you focus solely on the theory. Working in high demand research areas such as object detection, natural language processing etc also helps. ML in industry is all about using techniques developed in academia so there is an opportunity to understand the theory but people will not care so much about that, it will be more about debugging software, training models etc. Good luck and don't feel too old - a lot of uni academics don't have a background in DL as it is a comparatively new field and they are much older than you haha.

Haha

Looking at my own experience, I'd agree that my journal papers don't *directly* make the world a better place. I do get to do a fair bit of real-world-impact work though, like consult on sustainable transport systems - which sounds suspiciously commercial doesn't it? But I'm only in that position because academia let me build up a base of techniques and software that none of the commercial operators saw a business case for 10 years ago. (And also, because I've built some credibility based on those papers).

Google in Zurich or London pays more than in Mountain View (this is the one I know, not sure if Meta does the same)

Agreed, the salary difference is brutal (>3x more in industry after MSc, at least in ML…). My PhD experience was similar in terms of isolation. However, I think this does not necessarily have to be this way, it’s just how most academics unfortunately work :-/ advice would be to try to find groups which are more collaborative

that's a few companies in like two cities in Europe, definitely the exception to the trend there :p

I see (and agree with) your point (I am a European citizen working in the US) but ML work at that scale is not that common. If you want to stay local, academia and theoretical work are your best shots (with all the salary implications of the case).

danke :), das motiviert auch mich

ich hasse meine momentane studentische laufbahn so sehrrr...