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 reference:
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
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.
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 PANCURX -- BHKLAB”. All documents should be provided in PDF.
The research performed in the Bioinformatics and Computational Genomics Laboratory at the Princess Margaret Cancer Centre 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.
Lab website: http://www.pmgenomics.ca/bhklab/.
Dr. Benjamin Haibe-Kains, has over 10 years of experience in computational analysis of genomic data, including genetic and transcriptomic data. He is the (co-)author of more than 80 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 Hospital for Sick Children and Donnelly Centre.