I was admitted to several Phd position and I am considering most interesting two labs. Their fields are similar and both interesting to me. The problem is, one lab has more general topic, but the other has more specific topic.
The 'A' lab is focusing on brain-inspired AI. It aims to develop deep learning model based on neuroscience. There is not much rooms for mathematics seeing the previous papers. The 'B' lab is more concentrated on machine learning itself and uses extensive mathematics.
After I get Phd in CS, I will probably go to industry or perhaps to academia and I should be able to apply my knowledge to new domains which is required by company. I think mathematics is important for this flexibility.
I am worrying that if I study brain-driven AI during Phd, I might lack flexibility since the topic is specific and it is not using math a lot. Yes, I know that as a Phd, I should study mathematics by myself required for machine learning but it is also true that student whose topic is more math oriented ML will relatively have better knowledge at the end of Phd.
Some give me advice that I should go to a lab having more broad field of study. And it is better to go to more specific fields as a post-doctor. Considering the fact that I should always study new things by myself after Phd, I think it would be better to be trained with extensive mathematics which is core baseline of ML.
What do you think?