[Comp-neuro] PhD positions in the Neural Reckoning group at Imperial College

Dan Goodman d.goodman at imperial.ac.uk
Thu Oct 25 15:44:48 CEST 2018

One or more PhD positions in theoretical and computational neuroscience 
are available in the group of Dan Goodman at Imperial College London 
(http://neural-reckoning.org). 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. 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.

Suggested areas include:

* Spiking neural networks
* Auditory and other sensory systems
* Machine learning and neuroscience
* Simulation and data analysis

For more detail, see: http://neural-reckoning.org/openings.html

Applicants should initially send me a brief CV and cover letter with a 
description of research interests or a proposed project, and will 
eventually have to formally apply through the standard Imperial College 
mechanism (http://www.imperial.ac.uk/electrical-engineering/study/phd/).

There are no strict deadlines, although chances of success are higher 
for earlier applications as I will consider them as they arrive. EU 
students from outside the UK are particularly encouraged to apply before 
Christmas. Funded offers made before Brexit day (March 29) will be 
honoured by Imperial no matter what happens in the Brexit negotiations, 
and the last funding panel meeting before that will probably be in early 
February. So, applying before Christmas means we will have time to 
process the application and arrange an interview before that meeting.

Imperial College requirements:

* General requirements: 
* Country-specific minimum grades: 

Dan Goodman

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