GPU-Accelerated Computational Modelling and Simulation of Large-scale Biologically Realistic Models of the Fruit Fly Brain

Applications are invited for a PhD studentship starting in September 2016, which is aligned to the Digital Fruit Fly Brain project, a flagship research project funded by BBSRC and NSF. The project is led by the Centre for Signal Processing and Complex Systems (http://www.sheffield.ac.uk/acse/spcs) at University of Sheffield and by the Bionet Group at Columbia University (http://www.bionet.ee.columbia.edu/) in collaboration with a number of research laboratories in UK, US and Taiwan and supported by NVIDIA Corporation. The overall aim of the project is to design, implement and experimentally evaluate a potentially transformative open-source fly brain simulation platform capable of simulating ~135,000 neurons that make up the adult Drosophila brain. This computational infrastructure will be based on the recently established GPU-enabled Neurokernel software platform (https://neurokernel.github.io/). The modular simulation platform will integrate all knowledge about the Drosophila brain as a set of interconnected simulation modules which describe the operation of about 41 Local Processing Units (LPUs), 6 hubs and their interconnections, partly elucidated by detailed EM imaging studies. The simulation platform will be used to develop and validate a first draft model that incorporates the most advanced biophysical and/or functional models of the neurons and the latest published synaptic connections maps.

The focus of the PhD project is on the development of highly scalable algorithms that exploit the parallel processing power of multi-GPU systems to enable reverse-engineering and simulation of increasingly complex, large-scale, biologically realistic models of the fly brain using the Neurokernel platform. This will involve the development of methods and algorithms for neural modelling and data assimilation, mapping the computational models on large GPU clusters and adaptive load balancing. The project will offer the opportunity to work alongside other full time research staff as well as interacting with research teams from around the world.

The PhD student will work under the supervision of Dr Paul Richmond from the Department of Computer Science and Prof Daniel Coca from the Department of Automatic Control & Systems Engineering. The candidate will work alongside a team of three Research Associates already appointed.

Candidates must have an excellent first degree in Computer Science, Electrical/Control Engineering or a closely related subject and have excellent programming skills. Some prior experience of GPU programming or performance oriented computing is highly desirable. Previous experience in biological modelling, numerical simulation of complex systems, dynamical systems, would be an advantage.

Potential candidates are welcome to discuss their application informally with Dr Paul Richmond ([Email Address Removed])

Applicants should apply using the Apply button below or online at: http://www.sheffield.ac.uk/postgraduate/research/apply/applying