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BACKGROUND

We seek a postdoctoral fellow to work in collaboration with the Princess Margaret Head & Neck Cancer Research Group to discover biomarkers which will guide management of high-risk oral cavity squamous cell cancers, examining the role of tumor initiating cells, and develop novel future therapeutic strategies. The position involves integrative data analysis of our large multi-omics profiles of head & neck patient tumors generated in-house and through our international collaborations. Molecular profiles include whole-exome sequencing, mRNA and miRNA arrays of patient tumors and patient-derived xenografts. The project includes high-throughput discovery of prognostic and predictive biomarkers and molecular subtyping.

For representative projects please consider the following references:

Identification of a microRNA signature associated with risk of distant metastasis in nasopharyngeal carcinoma
Bruce JP, Hui AB, Shi W, Perez-Ordonez B, Weinreb I, Xu W, Haibe-Kains B, Waggott DM, Boutros PC, O'Sullivan B, Waldron J, Huang SH, Chen EX, Gilbert R, Liu FF.
Oncotarget. 2015 Feb 28;6(6):4537-50. PMID: 25738365

Comprehensive genomic characterization of head and neck squamous cell carcinomas
Cancer Genome Atlas Network.
Nature. 2015 Jan 29;517(7536):576-82. PMID: 25631445

REQUIREMENTS

Doctorate in computational biology, computer science, engineering, statistics, or physics. Published/submitted papers in 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. Expertise in R, C/C++ and Unix programming environments.

PREFERENCES

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).

ABOUT US

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 95 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.

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[at]utoronto.ca. The subject line of your email should start with "POSTDOC HNC – BHKLAB". All documents should be provided in PDF.

DEADLINE

Applications must be submitted before September 1st 2015.

Discussion forums: Opportunity: Postdoc Fellowship in Computational Cancer Biology: Translational Research in Head and Neck Cancer @ PM Cancer Centre -- Toronto, Canada

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