Dm'ed you

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!

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 :)

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 😅

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.

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.

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.

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.

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.

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.

Sure

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