Join us

PhD

I am interested in supervising students with a strong mathematical, computational or neuroscience background. Projects could be carried out in several possible areas relating to the work in the group. You might be interested in doing some general reading on computational neuroscience. Some suggestions for topics that would be interesting to me are below, but I'm very happy to consider other possibilities. In addition to working within the group, studying at Imperial College provides excellent opportunities for interacting with other theoretical and experimental researchers, both at Imperial and in the many neuroscience groups in London.

Apply for a PhD

Supervision style. It's important to select a PhD supervisor who you can work well with. My approach to PhD supervision is as follows. Students' projects are their own. I'm happy to make suggestions of things I find interesting and provide guidance, but I won't tell you exactly what to do. I would expect to see you on average around one hour per week, and this can either be at a regular time or arranged ad hoc. We have a weekly two hour lab meeting, lunch plus one hour doing either a journal club, tutorials, or presenting early stage research results for feedback. I would encourage you to get in touch with one of my current PhD students (see the list here) to have an informal chat about life in the group and at Imperial.

Postdoctoral

I do not currently have any open postdoctoral positions, but please get in touch if you are interested in applying for your own funding through a fellowship scheme, for example.

Themes and suggested topics

The main goal of the group at the moment is to understand computations based on the principle of sparsity in time and space. We are interested in this in both a neuroscience and machine learning setting. A key example is "spiking" neural networks comprised of elements that communicate sparsely in time and are connected sparsely in space. An example of the questions this brings up is: how do dynamically evolving networks of spatially located neurons compute given the communication and computational bottlenecks induced by that spatial arrangement?

To get a feel for this sort of work, check out the videos on my YouTube channel. In particular, take a look at the Cosyne tutorial I gave on spiking neural networks, and the longer Neuroscience for machine learners course I teach.

The list of questions below might give you some inspiration, but if you have another suggestion to make that you think I would be interested to supervise, please do get in touch because working with someone on a topic they are passionate about is always the most rewarding thing.