Postdoctoral Research Associate, Bioinformatics / Statistical Genetics
King's College London
The postholder will spearhead computational and statistical analyses of an extensive battery of genomewide SNP and exon-specific expression data (both array- and RNAseq-based) in various human brain tissues. The goal is to elucidate the genetic determinants of gene expression and alternative splicing in these different tissues.
There are many computational and statistical challenges. Efficient computational pipelines will be required in order to carry out these analyses in a timely manner. Expression levels among tissues will be highly correlated for some genes, and not for others (which will be of particular interest). Genes may co-express in some tissues, but not in others. A successful analysis strategy will intelligently adapt to all these scenarios, and there will be ample opportunity for methodological work. Competency in both statistics and especially programming (HPC computing, java/C, perl/python or equivalents) are essential requirements.
The post will be based at King's College London, Guy's Campus, but will closely interact with project colleagues at University College London, Institute of Neurology.
For an application pack and formal job description, please visit the KCL website at www.kcl.ac.uk/jobs or e-mail [log in to unmask] quoting reference G6/MGA/475/10-LA.
The post formally runs until 31 May 2013, but we have funds to extend this to 3-years subject to ratification by the MRC (pending).
For informal enquiries please contact Mike Weale ([log in to unmask]).
Closing date: 11 Nov 2010
Equality of opportunity is College policy
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Mike Weale, PhD
Senior Lecturer in Statistical Genetics
King's College London, Dept. of Medical & Molecular Genetics
8th Floor, Tower Wing, Guy's Hospital, London SE1 9RT
Tel: +44 (0)20 7188 2601 Fax: +44 (0)20 7188 2585
Web: www.kcl.ac.uk/schools/medicine/research/genetics/research/clusters/bse/weale
SGU info: www.kcl.ac.uk/schools/medicine/research/genetics/statisticalgenetics.html
SEGEG info: www.kcl.ac.uk/mmg/segeg
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