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Machine Learning Postdoc in Functional Genomics

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The new Statistical Machine Learning in Functional Genomics (Statomics) Lab of the Ottawa Institute of Systems Biology seeks a postdoctoral researcher who will, in collaboration with University of Ottawa faculty, develop and apply algorithms to solve current problems in analyzing and integrating gene-expression, proteomics, metabolomics, SNP, ChIP-chip, and/or clinical data. At present, the lab is targeting the inference of regulatory networks from multiple sources of information and improvements in the repeatability of microarray results and will attack similar machine learning challenges of importance to functional genomics. The researcher’s background will complement that of any students and any postdoctoral researcher to be recruited to the Statomics Lab from the statistics community, creating an interdisciplinary environment for high impact on the biological sciences as well as on machine learning.

Scientific creativity and a thorough knowledge of validation techniques and machine learning methods of data analysis (such as those of random forests, support vector machines, and Bayesian belief networks) are essential, as is the demonstrated ability to quickly and accurately implement their algorithms in software. Strong initiative, excellent communication skills, and reception of a PhD in bioinformatics, computer science, mathematics, physics, or a closely related field within the four years prior to the start date are also absolutely necessary. The following qualities are desirable but not required: knowledge of biology; familiarly with BUGS, R, S-PLUS, 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 dbickel0@uottawa.ca (without the zero), with “machine learning postdoc” and the year of your graduation or anticipated graduation in the Subject line of the message; in the plaintext message body, concisely include evidence that you meet each requirement for the position and a description of your most significant papers and software packages with an explanation of your own contributions to them. Only those applicants selected for further consideration will receive a response.

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