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Getting job offer while waiting for news from school


pikachu

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Hey-hey, guys :)

well, the situation is that I applied to CS masters program to École Polytechnique Fédérale de Lausanne. it is absolutely a dream to get there.

But it is kinda unreal. Because.. hmm. I am very not outstanding, mediocre, average, nothing special, can continue endlessly :D

And... most important thing :) I got rejection from this school 2 years ago. Well, I worked a bit on my personal statement, refined my interests, got more work experience (industrial, not research...), learnt French :)  But in general I am the same person. Didn't become super_sexy_applicant.

But I applied. And waiting for results that should show up by March.

And... I got very-very nice job offer... and they want my immediate answer, next week is the deadline, since I should join the project start-up there :(

And I don't know what to do. Yes, probably, I should go with work proposal... anyway, I won't be accepted to EPFL. But what if... And this thought doesn't go out of head.

What would you do in such situation?

Thank you very much for reading this. Feeling much better that I can share... :)

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Based on friends that I know of, I would take the job offer instead of waiting for your dream school.

Unless you wanted to be a professor or a PhD, otherwise, there are not too many reasons for you to go into a graduate program in CS (not to mention that you do not know if the school will accept you). If your intention to get a master degree is to make more money, then I'm pretty sure a bachelor degree in CS won't make any less money than a master degree. That being said, if it takes you 2 years to complete your master program, then you'll make $180K USD less in this 2 years (if you are working in Amazon). Now, can you make that $180K back (that you lost in the 2 years) with your master degree, before you move onto a management position (possibly getting a MBA when you are 30)?

You can also ask for a deferral if EPFL accepts you after you take the job.

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I would tend to disagree about the job offer. As soon as I see the words "start up" I tend to run for the hills. Being a start-up, the very first thing you should do is make sure they're gonna pay you in cash, not "umm well you can have a percent of our company that's gonna fail in a couple years anyway". I've just seen too many people who just learned PHP or whatever and think they're gonna make the next Facebook or something.

I'd say if they can't wait what is a fairly reasonable amount of time for you to tie up some loose ends that, in all fairness, existed long before they offered you a job, then to heck with them IMO. They need to recognize that you had other things going on before they popped out of the blue and offered you a job.

Also, it's always good to keep in mind what university is and isn't, especially at the graduate level: it is an opportunity to enrich yourself, to pursue research that presumably you are very passionate about. It is not just another two years of job training. Ask yourself this: would I go through with my masters degree even if I knew that it would not make me do any better in the job market? If yes, then you are doing it for the right reasons. I think too many people merely see this kind of education as a means to an end, rather than evaluating it on its own merit.

Edited by hall1k
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aberrant, hall1k,

thank you soooo much for your answers! feeling not alone in making hard decisions.

actually it is not really cool job like in Amazon, Google, etc. It is just promises-to-be-interesting job... in a nice team of guys. Nothing overambitious :)

Having BS in software engineering, I want to get masters in order to learn more about machine learning. Though it is not specifically machine leaning masters, but I can choose there a lot of very interesting courses.

Guys, you gave me a lot of info to think of. thank you again!Will go to walk and reflect a bit

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It is good that you're thinking about it instead of just rushing into it. Startups in particular are very risky. You probably have better odds of getting into that dream school than the company taking off. Keep in mind that over half of new companies fail in the first year or two.

Also I think you will very much enjoy machine learning. I do a lot of pattern recognition and classification as part of my bioinformatics research, and in order to understand the theory behind it you need a good understanding of calculus and especially linear algebra, along with probability (particularly from the Bayesian perspective). Depending on what courses you took in undergrad, you may want to brush up on linear algebra before you start in September, because (at least at my undergrad) we only took basic linear algebra in first year, and nothing after that. I was absolutely floored the first time I saw the equation for linear dimensionality reduction. It takes up three lines of a page!

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I would go to school. Once you join the work force, stopping and going through the application process is very hard. Believe me, I'm doing it now and what I thought would take a year has taken 3. Get more education. In your field, it will probably translate to a higher starting salary and better jobs.

...and Lausanne is charming!

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I always think of it like this: Education is the single best investment you can ever make (well, in a real field, anyway). Unlike a car, a house, or any other physical good, it cannot possibly be repossessed if you default on your loans. No-one can take it away from you once you have it.

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No problem! I know I can be a little... harsh with my opinions, but sometimes that's what's needed. Trust me when I say that there will be plenty more startups to jump into later on. They're like locusts in the CS world lol.

If you like, I'd recommend this paper I just finished reading (written by my current advisor) on linear dimensionality reduction. Between LDR and SVM methods, you should have most of the machine learning covered, as well as HMMs. Neural networks are also very interesting, but have fallen out of favor lately, as most people use HMMs instead. There's also a book called Pattern Classification by Peter Duda (sp?) that gives a good overview.

http://janus.newcs.uwindsor.ca/~lrueda/papers/ChernoffLDATwoJnl.pdf

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