Postdoctoral Fellowship in Advanced Bayesian Computation for Cross-Disciplinary
Research
An EPSRC-funded postdoctoral fellowship at Warwick Systems Biology Centre is
available from April 2013 to work on advanced Bayesian computational methods
for bioinformatics and systems biology.
The successful candidate will be part of a interdisciplinary research project
to develop advanced statistical theory and algorithms that will directly
address current challenges in scientific modelling in molecular biology,
astronomy and econometrics. This research project is a collaboration between
experts in computational biology (Prof. David Wild, Warwick), statistical
machine learning (Prof. Zoubin Ghahramani, Cambridge), statistics and
econometrics (Dr. Jim Griffin, Kent) and astronomy (Prof. Andrew Liddle,
Sussex).
A variety of projects are possible, depending on the background and interests
of the candidate, and include:
Applications of nonparametric Bayesian modelling to a number of
contemporary problems in computational biology, including:
- static and time-varying graphs, such as molecular structures and
dynamic regulatory networks
- data integration from multiple molecular phenotype platforms,
such as transcriptomics, proteomics, and metabolomics
The development of new methods for Bayesian experimental design
The construction of efficient algorithms for inference in
high-dimensional, highly-dependent structured data sets, based on GPU
computation.
Warwick Systems Biology Centre is co-located with the Systems Biology and MOAC
(Molecular Organisation and Assembly in Cells) PhD Programmes and offers a
thriving research and postdoctoral training environment at the interface of the
life sciences and the mathematical and physical sciences.
Candidates should have a Ph.D. in either computational biology or a relevant
quantitative field such as statistics, applied mathematics, theoretical
physics, theoretical chemistry or computer science and a strong interest in
molecular biology. Good programming skills in a high level language such as
Matlab, R or C/C++ are essential and previous experience of Bayesian methods
and MCMC would be an advantage.
For informal discussions, and applications, please contact Prof. David Wild
([log in to unmask]) in the first instance. Applicants should include a
full CV and accompanying letter outlining their interests and any previous
work.
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