Computational modelling of spiking neural network self-organization

A PhD studentship is available in the lab of Dr Roman Bauer at the Department of Computer Science at the University of Surrey (Guildford, UK). The successful candidate will use computer simulations to study how spiking neural networks can give rise to function, such as classification/prediction of spatio-temporal (visual or auditory) signals. This will contribute to novel technologies for neurally inspired computing, and help shine light shine on how neuronal circuits in the brain operate.

This particular PhD studentship will be conducted in co-operation with the BioDynaMo collaboration, where open-source code for the neuroinformatics/computational neuroscience research community is developed.

Successful applicants will become part of a vibrant PhD community and will benefit from the strong research environment and high international visibility of the Department. Its researchers publish regularly in top conferences and journals and are renowned experts in their fields, developing innovative, practical solutions to real world problems. The Department has strong links with industry and active collaborations with academic institutions worldwide.

Background: The ideal candidate will have experience in computer science / physics / mathematics or similar. Proficiency in an object-oriented programming language is required. Previous experience in the modelling and simulation of spiking neural networks would be desired. 

Funding: Each PhD studentship comes with a stipend of £16,000 per annum plus tuition fees covered for the duration of 3 years for UK/EU candidates. International candidates are welcome to apply but will need to cover the difference between the UK/EU and overseas fees.

More details including instructions for the application can be found at: https://www.surrey.ac.uk/fees-and-funding/studentships/phd-studentships-computer-science. Interested candidates are encouraged to contact the lead supervisor Dr Roman Bauer (r.bauer@surrey.ac.uk).

Application deadline is 21st of February, 2021.