A two-year postdoctoral position, available to start immediately, is available in the Valdar Lab, Department of Genetics, University of North Carolina at Chapel Hill.

The postdoctoral fellow will work primarily on the statistical and computational analysis of data arising from experiments that investigate gene-by-drug and gene-by-treatment interactions in genetically heterogeneous populations, namely in the Collaborative Cross genetic reference population. The postdoc will based in the Valdar lab, and will focus on two exciting collaborative projects: one on toxicogenomics with researchers at The Hamner-UNC Institute for Drug Safety Sciences (Watkins/Mosedale group, and the other on genetic susceptibility to infectious diseases, with collaborators in the Dept of Microbiology (Heise) and the School of Public Health (Baric) at UNC. These projects are expected to involve analysis of phenotypic and gene expression data. In addition, the postdoctoral fellow will work closely with Dr. Valdar and his group, and will be expect to contribute to the development of statistical and/or computational methods related to the analysis and design of these and other genetic experiments.

Salary will depend on experience and background and will follow NIH postdoctoral guidelines when relevant.

To submit your application, please click on the "Apply through website" button.

The required application materials include:

1. A cover letter explaining your interests and suitability for the position.
2. A curriculum vitae, including the names of three potential referees.
3. An example (or examples) of your written work such as manuscripts (under preparation, in press, or published) or technical reports. Work in which you are the lead author are strongly preferred.

Applications will be considered as they arrive. Letters of recommendations will be requested from a subset of applicants only after an initial reviewing period.

The ideal candidate will have a PhD in a quantitative field, eg, statistics, biostatistics, statistical genetics, computer science, statistical bioinformatics, and experience of (or strong interest in) the analysis of genetic and/or gene expression data, as well as good written and oral communication skills. Proficiency in one or more programming languages is essential, with preference is given to those with a strong working knowledge of R and UNIX with experience of compiled or semi-compiled languages such as C, C++, Python, etc, being an advantage. The candidate should have a strong working knowledge of statistical methods, ideally with exposure to any/all of the following: mixed models, hierarchical modeling, Bayesian methods and computation, hidden Markov models, computationally intensive statistics.

The Valdar Lab focuses on statistical and computational methods for dissecting the genetic basis of complex disease. Recent work includes: hierarchical Bayesian models to quantify genome-wide and locus-specific inheritance in mouse genetic crosses, Bayesian causal analysis of gene-by-drug interactions, penalized regression approaches to fine-mapping in human GWAS, and detection of QTL in multiparent populations. The lab is based in the Department of Genetics, which was established in 2000 to foster cutting edge research in genetics and genomics, and collaborate extensively with groups in the departments of Statistics, Biostatistics and Computer Science, as well as groups nationally and internationally.