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BACKGROUND

We seek a postdoctoral fellow for a project that aims at finding solutions to the high fatality rate of pancreatic cancer by analyzing genomic and biologic subsets of disease, mechanisms of tumorigenesis, and tailored treatment options. The position involves integrative data analysis of PanCuRx, the largest genomic profiling project for pancreatic cancer, as part of the International Cancer Genome Consortium. Molecular profiles include whole-genome sequencing and RNA-seq for a large cohort of micro-dissected patient tumors and patient-derived xenografts. The project includes high-throughput discovery of prognostic and predictive biomarkers, molecular subtyping and drug repurposing.

For representative projects please consider the following references:

Genomic analyses identify molecular subtypes of pancreatic cancer
Bailey P, Chang DK, Nones K, ..., Waddell N, Biankin AV, Grimmond SM.
Nature. 2016 Mar 3;531(7592):47-52. PMID: 26909576

Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes
Biankin AV, Waddell N, ... , Pearson JV, McPherson JD, Gibbs RA, Grimmond SM.
Nature. 2012 Nov 15;491(7424):399-405. PMID: 23103869

Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy
Collisson EA, Sadanandam A, Olson P, Gibb WJ, Truitt M, Gu S, Cooc J, Weinkle J, Kim GE, Jakkula L, Feiler HS, Ko AH, Olshen AB, Danenberg KL, Tempero MA, Spellman PT, Hanahan D, Gray JW.
Nat Med. 2011 Apr;17(4):500-3. PMID: 21460848

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 PANCURX – BHKLAB". All documents should be provided in PDF. Deadline for submission: July 1, 2016

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