PhD student here doing neuromorphic computing and running deep learning models on analog hardware. The 'neuromorphic computing" is a vaguely defined term, which

PhD student here doing neuromorphic computing and running deep learning models on analog hardware. The 'neuromorphic computing" is a vaguely defined term, which can mean anything from developing novel Spiking NN models to the design of hardware capable to run those models (e.g. TrueNorth). Wetware computing is probably even lesser understood area. Neither has much to do with deep learning field, which does not use spiking NN computation. On the other hand, there are very promising research directions precisely in the intersection of neuroscience and deep learning (e.g. biologically plausible credit assignment problem, learning algorithms of the brain, objective functions in the brain, sensori-motoric integration, etc). It's rare to find a person with solid understanding of both fields, especially in academia. If I were you I would look at residency programs at big companies (Google Brain, Facebook AI fellowship, etc). Also, there's a company called Numenta which is probably looking for people with your skills (they are trying to reverse engineer neocortex' algorithms). From what I heard, those places give you some freedom to choose your own problems, encourage publishing papers, and provide very bright people as mentors.