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BACKGROUND

We seek a postdoctoral fellow for a project that aims at quantitatively extracting features from imaging data sets (MRI, PET-CT, CT scans) to assess the correspondence with molecular profiles and clinical parameters and outcome of patients. A combination of automated and manual image characterization methods will be used to characterize these quantitative imaging biomarkers, using a so-called "radiomics" approach+++. The goal is to develop and evaluate novel computational imaging processing methods applied to existing and future datasets from throughout North America and Europe. This position will allow for close collaboration with world leading experts in the fields of imaging, image analysis, bioinformatics, computational biology, machine learning, and artificial intelligence. The candidate will be directly supervised by Dr. Benjamin Haibe-Kains and co-supervised by Drs. Fei-Fei Liu (University Health Network, Canada) and Hugo Aerts (Dana-Farber Cancer Institute, USA).

+++ Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Nat Commun. 2014 Jun 3;5:4006. PMID: 24892406

REQUIREMENTS

Doctorate in Engineering, Physics, Bioinformatics, Computer Science, or related subject, with an interest in advanced image analysis, artificial intelligence, and machine learning. 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). An understanding of image acquisition and reconstruction protocols and standardization would be helpful.

ABOUT US

Labs:
Research in the Haibe-Kains lab is focused on the development of novel computational approaches to best characterize carcinogenesis, drugs' mechanisms of action and their therapeutic effects 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. We are collaborating with the Aerts lab to apply machine learning approaches in the context of Radiomics. 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 the lab website for further information: http://www.pmgenomics.ca/bhklab/

Research in the Liu lab is focused on translational molecular oncology, including the development of biomarkers for head & neck cancers, high-throughput screens to identify novel anti-cancer therapeutics, and stem cell regenerative therapy. Using DNA, RNA, and protein extracted from diagnostic human cancer samples, we are in the process of conducting multi-omic global expression analyses. See the lab website for further information: http://www.uhnresearch.ca/labs/liu/

Research in the Aerts lab is focused on the integration and analysis of various types of data for personalized medicine, specifically from imaging and genomic data. See the lab website for further information: http://www.cibl-harvard.org

Lab Directors:
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.

Dr. Fei-Fei Liu is Chief and Chair, and Professor in the Departments of Radiation Oncology, Medical Biophysics and Otolaryngology at the University of Toronto. She holds the Dr. Mariano Elia Chair in Head and Neck Oncology. For an exhaustive list of publications, go to Dr. Liu's Google Scholar profile.

Dr. Hugo Aerts has a broad background in engineering, with specific expertise in the extraction and analysis of medical imaging data, in bioinformatics and in genomic data analysis. For an exhaustive list of publications, go to Dr. Aerts' 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 RADIOMICS – BHKLAB". All documents should be provided in PDF.

Discussion forums: Opportunity: Postdoctoral Fellowship in Cancer Radiomics @ Princess Margaret Cancer Centre -- Toronto, ON, Canada

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