Regarding switch from pure mathematics to machine learning: opinions and information I'm a first year postdoc in pure mathematics (geometry/topology with strong background in analysis) with undergraduate statistics and probability background (with also measure theoretic probability). I also have some programming knowledge in ForTran, C and Matlab, but I never used them in my pure mathematics career in my graduate school.
In my next job, I'm considering doing a postdoc in machine learning. The reason behind this switch is: I'm satisfied with my pure knowledge so far and have been wanting to  see some real-life applications of mathematics, and also keep my options open in both industry and academia. 
My questions are: 
1) How hard is the switch going to be? I guess I've all the required mathematics background, but will it be hard to pick up the necessary computer science skills, even if I work in more theory-oriented problems?
2) Is there a website/email-list where I can get notifications on jobs in machine learning? I'm looking for jobs in Europe mostly.
Thanks and much appreciation :)
 A: 1) Sure you can make the change. I understand that you want something more applied but maybe for a first postdoc you should find something closer to maths in order to avoid having writing thousands of lines of code (for which, most probably you don't have the required skills yet). Try to find a project that has some serious application you're interested in; that they need someone with more mathematical background (that will be your advantage over competition) and; try being part of a team that can support you in the more applied part of the job while you can get the most out of them in preparation for your next step.
In the last EU research project I was involved there were all the flavours of backgrounds: mathematicians (that have made some similar switch to the one you want to make), and computer scientists with more solid engineering skills. The mathematicians worked more on the theoretical foundation of ideas and approaches while the CS people were deploying the solutions to real world applications - most of the times it was visa versa, i.e., CS engineers were doing some tricks to solve the problem, maths came afterwards to build theory to support the tricks. There was some sincere collaboration, but also there were some distinct cultures and approaches to solving problems for which you should be prepared...
2) The mail list you're after is the ml-news@googlegroups.com (https://groups.google.com/forum/#!forum/ml-news).
A: You can. I'll suggest to learn some python (for pandas and scikit learn package) and R (lot's of usefull packages) because even if you want to code your own method, you will have all the benchmark you need already coded in their packages. Also, these two are the best for manipulating data. Some o'Reilly books can help you with that.   
