We seek a postdoctoral fellow for a project that aims at analyzing an in-house compendium of pharmacogenomic datasets to identify drug combinations with high therapeutic effects in multiple cancer subtypes. Our database comprises ~1,500 cancer cell lines treated with up to 45,000 drug compounds, constituting the largest pharmacogenomic database to date. This large amount of data provides a unique opportunity to better explore the large space of combination therapies and validate the most promising candidates in organoids and in patient-derived xenografts.
For representative projects please consider the following references:
Haibe-Kains B, El-Hachem N, Birkbak NJ, Jin AC, Beck AH, Aerts HJ, Quackenbush J.
Nature. 2013 Dec 19;504(7480):389-93. PMID: 24284626
Papillon-Cavanagh S, De Jay N, Hachem N, Olsen C, Bontempi G, Aerts HJ, Quackenbush J, Haibe-Kains B.
J Am Med Inform Assoc. 2013 Jul-Aug;20(4):597-602. PMID: 23355484
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. Expertise in R, C/C++ and Unix programming environments. Wet lab experience in drug screening is a major asset.
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 firstname.lastname@example.org. The subject line of your email should start with “POSTDOC DRUGCOMB -- BHKLAB”. All documents should be provided in PDF.
Applications must be submitted before September 1st 2015.
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/
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.