Post-Doctoral Opportunities in Computational Statistics

Post-Doctoral Opportunities in Computational Statistics

Posted by Rebecca Martin on Sun, 02/05/2010 - 00:00

As a result of recent research grant awards there are outstanding opportunities at the Inference Group of the University of Glasgow for two ambitious post-doctoral research associates with an excellent research background in computational statistics. Both of these posts are of three years duration in the first place. A further post-doctoral position will become available within the group later in the year.

The Inference Research Group at Glasgow is at the forefront of developing advanced statistical methodology for scientific applications in particular at the life-sciences interface and presents tremendous substantive collaborative opportunities with major biological research groups in Inflammatory Response, Cancer Research, Pathway Signaling, Parasitology, Cardiac Systems, Plant Biology, and Medical Statistics. The group led by M. Girolami is very well resourced with current research income in excess of £2M and two dedicated high-performance computing clusters supporting seven post-doctoral researchers and six PhD candidates.

Informal enquiries and further details are available from Prof Mark Girolami (girolami(at)dcs.gla.ac.uk)

Position. 1. Research Associate
We seek a post-doctoral research associate to work on the development and application of advanced statistical methodology for nonlinear dynamical system models of biochemical processes. This will be as part of an exciting and ambitious integrated multi-disciplinary project studying the metabolism of the parasitic protozoan Trypanosoma brucei. The project will be led by Professor Mark Girolami at the University of Glasgow.

The purpose of the position is to develop theory and methods for appropriate Markov Chain Monte Carlo (MCMC) based inference over nonlinear differential equations that will exhibit high degrees of ‘evolutionary redundancy’. Other areas of investigation will include model-based experiment design and software emulation as well as cluster-based computation for MCMC.

The focus of application will be on practical inference over dynamic system models of metabolic and protein based regulation in trypanosomes as they respond to oxidative stress with particular emphasis on pathways implicated in redox metabolism in these parasites.

The project is part of a pan-European programme to determine regulation of these pathways at the levels of transcription, translation and metabolic control in conjunction with world leading biologists.

The ideal applicant will have experience in the development and application of MCMC methods for Bayesian inference, experiment design and software emulation. A genuine interest in Systems Biology is required. Some background in high performance computing development for MCMC would be preferred.

This post is linked to the BBSRC funded project The Silicon Trypanasome

Informal enquiries and further details are available from Prof Mark Girolami (girolami(at)dcs.gla.ac.uk)

Position. 2. Research Associate
We seek a post-doctoral research associate to work on developing Bayesian methods to assist in the systematic and rational choice of mathematical models describing natural systems in population and systems biology. This will be part of a collaborative three-centre BBSRC funded research project that includes the University of Glasgow, Imperial College London, and Royal Holloway University of London.

The purpose of this position is to build upon the work reported in the recent Science signalling paper from the Girolami group (http://stke.sciencemag.org/content/vol3/issue113/cover.dtl) where the rationale design of biological experiments to assess the evidential support for competing signalling pathway topologies was enabled employing estimates of Bayes factors. From a methodological perspective the development of probabilistic methods for the solution of differential equations and the exploitation of the recently developed Riemann manifold Monte Carlo methods along with current advances in estimating marginal likelihoods is anticipated.

The ideal applicant will have an extensive background in the development of Bayesian methodology and have recent experience at the life sciences interface.

This post is linked to the BBSRC funded project Inference-based Modelling in Population and Systems Biology.

Informal enquiries and further details are available from Prof Mark Girolami (girolami(at)dcs.gla.ac.uk)
This post has initial funding for 36 months