BACKGROUND

Join our team to map and mine large-scale genetic interaction networks! Two computational biology postdoc positions are available, one in the Myers lab at the University of Minnesota (Minneapolis) and one in the Boone lab at the University of Toronto.

The successful candidates will have the opportunity to work on an exciting project mapping and analyzing global genetic interaction networks in S. cerevisiae and S. pombe. Our focus will be to develop computational approaches for mining and comparing these networks and integrating them with other genomic data. Our labs are using these powerful data to address a variety of questions including the evolution of gene duplicates and network modularity, mapping drug targets through comparison with chemical genetic data, integration of genetic interactions with high-content phenotype screens, and building cross-species predictive models to infer interactions in the context of cancer.

Both postdocs will work together with a team of computational and bench biologists to support collaborative projects across the two labs.

REQUIREMENTS

The ideal candidate would have a computational background with a Ph.D. in Computer Science, Statistics, Physics, Math or Molecular Biology. Substantial experience programming in R, Matlab, Perl, Java or C++ is required. Basic understanding and experience in systems/network biology is preferred. Previous experience in data mining and/or machine learning is a plus. Strong verbal and written communication skills and a desire to be involved in highly collaborative research are essential.

LOCALE

University of Minnesota, Minneapolis, Minnesota, USA OR
University of Toronto, Toronto, Canada

HOW TO APPLY

Interested applicants should email Chad Myers (cmyers[at]cs.umn.edu) and Charlie Boone (charlie.boone[at]utoronto.ca). Please include a CV, contact information for at least two references, and indicate when you are able to start (as soon as possible is preferred).