Area: interface between bio-informatics, applied mathematics, and statistical mechanics
data integration, machine learning, Bayesian analysis, gene arrays
Duration: 3 years
Starting date: Sept 2009
Closing date for applications: June 12th 2009
The aim of the project is to perform a Bayesian classification and regression analysis by integrating data representing tumour image traits (MRI scans, mammograms, FRET/FLIM, and PET images) with gene expression profiles, in order to predict clinical outcomes and treatment response for breast cancer patients. We also expect to study the gene regulation networks involved in the disease progression, possibly using additional public data sets.
The candidate will assess existing statistical and machine learning methods to extract and integrate information from gene array, image traits and biophysical experiments. He/she must be able to develop and implement new or improved algorithms to make the best use of available data. The candidate will also perform mathematical investigations of gene regulatory networks and their disruptions caused by cancer.
Ideally, the candidate for this position would have a very solid mathematical background, preferably in statistical mechanics and/or statistical (machine) learning theory, and have experience in and affinity for dealing with biological data. The candidate must have scientific programming experience, and be comfortable with communicating with scientists from different backgrounds (mathematicians, biologists, physicists, and physicians).
Dr E Blanc
MRC Centre for Developmental Neurobiology
and KCL Centre for Bioinformatics (KCBI)
eric.blanc (at) kcl.ac.uk
Prof ACC Coolen
Department of Mathematics
ton.coolen (at) kcl.ac.uk