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Postdoctoral training in Bayesian genomics

27 July 2011

Reliable interpretation of genomic information makes unprecedented demands for innovations in statistical methodology and its application to biological systems. This unique opportunity drives research at the Statomics Lab of the Ottawa Institute of Systems Biology (http://www.statomics.com) to marshal strengths of robust Bayesian, empirical Bayes, and frequentist frameworks. The lab seeks a postdoctoral fellow who will collaboratively develop and apply novel methods of Bayesian inference to overcome current challenges in learning from genome-wide association data, high-dimensional gene expression data, and other data related to genomics.

Experience in computationally intensive data analysis is essential, as is the ability to quickly design and code reliable software implementing Markov chain Monte Carlo algorithms. Strong initiative, excellent communication skills, and reception of a PhD or equivalent doctorate in statistical genetics, statistics, bioinformatics, computer science, mathematics, physics, any field of engineering, or an equally quantitative field within four years prior to the start date are also absolutely necessary. The following qualities are desirable but not required: working knowledge of statistical genetics or genomics; familiarly with R, S-PLUS, Mathematica, C, Fortran, and/or LaTeX; experience in a UNIX or Linux environment.

To apply, send a PDF CV that has contact information of three references to dbickel@uottawa.ca, with “Bayes Postdoc” and the year of your graduation or anticipated graduation in the subject field of the message. In the message body, concisely present evidence that you meet each requirement for the position and describe your most significant papers and software packages with summaries of your contributions to them. All applicants are thanked in advance; only those selected for further consideration will receive a response.