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Research Associate
University College London, UK

Company Information: Our group is part of the Department of Genetics, Evolution and Environment (GEE) and the UCL Genetics Institute (UGI), a vibrant centre of excellence in medical, statistical and computational genetics, offering one of the most exciting work environments in the UK. GEE is a large and collegial Department, which embraces essentially all aspects of modern biology.

Position Title: Research Associate in Statistical Genetics and Bioinformatics Ref: 1645026

Duties and Responsibilities: We are seeking a talented postdoctoral biostatistician/statistical geneticist to investigate the genetic architecture of obesity and diabetes in animal models of these important human diseases. The principal duties involve the statistical genetic analysis of data arising from a study of obesity and diabetes in a population of outbred heterogeneous stock (HS) rats. The data comprise over 20 obesity and diabetes traits, transcriptome data from liver and adipose tissue and genome-wide genotype information collected in these animals. The analysis will use statistical genetic methods, previously developed by our group (Mott et al 2000 PNAS, Yalcin et al 2005 Genetics, Durrant and Mott 2011 Genetics, Davies et al 2016 Nature Genetics) and others (eg Gatti et al 2014) to find regions of the genome that affect outcomes related to diabetes and obesity. Specifically, this person will identify quantitative trait loci (QTL) for both phenotypic and transcriptome data in order to determine the gene networks which correlate with disease.

This NIH-funded project is a collaboration between Richard Mott at University College London (UCL) (see ) and Dr. Leah Solberg Woods at the Wake Forest School of Health, North Carolina USA (see The post-holder will join Richard Mott’s group at UCL, a dynamic group working on quantitative and population genetics across a wide range of animal and plant species.

Position Qualifications: Applicants should have relevant scientific education (PhD degree in Biostatistics, Computational Biology, Statistical Genetics or related field), preferably with a publication track record. Knowledge of applied statistics using R and programming experience is necessary, as well as good written and oral communication skills.

Salary Range: £30-£40k per annum depending on experience.

Benefits: The post is funded for 2.5 years in the first instance.


Application Information: For more details and to apply follow the link

Contact Email:

Application Deadline: 06/07/2017