[Neuroinfo] PhD Studentship on ‘GPU-Accelerated Computational Modelling and Simulation of Large-scale Biologically Realistic Models of the Fruit Fly Brain’

[Paul Richmond]

Background

Applications are invited for a PhD studentship starting in September 2015,
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 (
www.sheffield.ac.uk/acse/spcs) at University of Sheffield and by the Bionet
Group (http://www.bionet.ee.columbia.edu) at Columbia University, 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.

Project Description

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 fruit 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.

Candidate Profile

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.

How to apply

To apply please submit a PhD application using our online application
system via the Apply link at the following:
http://www.sheffield.ac.uk/postgraduate/research/apply/applying

Your application should include a CV containing the names and addresses of
two referees, along with a 150-word (maximum) description of your relevant
experience for this research programme.

You should indicate this project and enter Professor Daniel Coca as your
proposed supervisor in your application. If you wish to discuss any details
of the project informally, please contact either Professor Coca or Dr
Richmond as stated above.

-- 
Dr Paul Richmond (Research Fellow) - University of Sheffield
http://www.paulrichmond.staff.shef.ac.uk
p.richmond at sheffield.ac.uk

Department of Computer Science <http://www.sheffield.ac.uk/dcs>
Sheffield NVIDIA CUDA Research Centre
<http://gpucomputing.sites.sheffield.ac.uk/>
Advanced Computing Research Centre <http://www.acrc.com>
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