Postdoctoral Fellowship in Computational Cancer Biology: Biomarkers in Breast Cancer

Organization
University Health Network
Job Location
101 College Street
Princess Margaret Research Tower, 11-310
Toronto, ON M5G1L7
Canada
Benefits

UHN employees have one of the best benefits in Canada (http://www.uhn.ca/).

Job Description

We seek a postdoctoral fellow to develop novel integrative approaches to develop molecular predictors of response to approved and novel drugs for breast cancer. We have generated high-dimensional molecular (mutations, copy number variations, transcriptomics, epigenomics) and pharmacological (drug sensitivity) profiles of a large panel of breast cancer cell lines. We are profiling patient-derived xenografts as well, therefore providing a unique opportunity to explore the biology underlying drug response, and develop predictors that can be validated in vivo before their use in clinical trials.

Requirements

Doctorate in computational biology, computer science, engineering, statistics, or physics. Published/submitted papers in cancer genomics and/or machine learning research. Experience with analysis of high-throughput omics data, such as next-generation sequencing and gene expression microarrays, in cancer research. Very strong expertise in programming and machine learning (R, C/C++ and Unix programming environments).

Preferred:

Hands-on experience in high performance computing, especially for parallelizing code in C/C++ (openMP) and/or R in a cluster environment (Sun Grid Engine/Torque).

How to Apply

Submit a CV, a copy of your most relevant paper, and the names, email addresses, and phone numbers of three references to benjamin.haibe.kains@utoronto.ca. The subject line of your email should start with “POSTDOC BCPRED -- BHKLAB”. All documents should be provided in PDF.

About Our Organization

Lab

Our research focuses on the development of novel computational approaches to best characterize carcinogenesis, drugs’ mechanisms of action and their therapeutic potential, from high-throughput genomic data. We have strong expertise in machine learning applied to biomedical problems, including the development of robust prognostic and predictive biomarkers in cancer. Our large network of national and international collaborators, including clinicians, molecular biologists, engineers, statisticians and bioinformaticians, uniquely positions us to perform cutting-edge translational research to bring discoveries from bench to bedside. See our lab website for further information: http://www.pmgenomics.ca/bhklab/

Lab director

Dr. Benjamin Haibe-Kains, has over 10 years of experience in computational analysis of genomic data, including genomic and transcriptomic data. He is the (co-)author of more than 100 peer-reviewed articles in top bioinformatics and clinical journals. For an exhaustive list of publications, go to Dr. Haibe-Kains’ Google Scholar Profile.

Princess Margaret Cancer Centre

The Princess Margaret Cancer Centre (PM) is one of the top 5 cancer centres in the world. PM is a teaching hospital within the University Health Network and affiliated with the University of Toronto, with the largest cancer research program in Canada. This rich working environment provides ample opportunities for collaboration and scientific exchange with a large community of clinical, genomics, computational biology, and machine learning groups at the University of Toronto and associated institutions, such as the Ontario Institute of Cancer Research,  Hospital for Sick Children and Donnelly Centre.

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