MRC-funded Ph.D. Studentship in Computational Neuroscience

MRC-funded Ph.D. Studentship in Computational Neuroscience

Closing Date: 
Monday, April 24, 2017

Applications are invited from UK or EU residents with a strong mathematical or computational background who wish to use their skills to understand how the brain works or how we can better treat neurological conditions.

There is currently a huge potential of combining mathematical, computational and biomedical approaches to solve human health challenges. Within the last few decades, neuroscientists have gained detailed knowledge of the biology of the brain, and the time is ripe to create formal theories describing its functioning. Such theories would play a similar role as laws of physics, and would help to predict how to best restore normal information processing in patients affected by diseases.

The doctoral studies in computational neuroscience would start in October 2017. The project will be supervised by Rafal Bogacz and will be carried out within the interdisciplinary team of the MRC Brain Network Dynamics Unit. The topic of research could range from developing a formal theory of information processing in the brain to applying machine learning to the data recorded from the brains of patients.

Candidates must have a very strong mathematical or computational background and an excellent undergraduate degree in a relevant discipline (e.g. computer science, engineering, mathematics, physics or neuroscience). An undergraduate 1st class or upper 2nd class degree (or equivalent grade) is required for graduate entry to the University of Oxford. The minimum standards for English apply for applicants. This PhD (DPhil) studentship is funded by the Medical Research Council.

Formal applications must be made via the University online application system. The relevant course code is: Doctor of Philosophy in Clinical Neurosciences – RD_CU1. In the covering letter, please describe the research you would like to conduct and clarify how your experience and skills would help you in that work. The closing date for applications is 12.00 noon on Monday 24th April 2017.

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