Job:Postdoctoral Fellowship in Cancer Radiomics + Genomics
Entering edit mode
7 weeks ago
bhaibeka ▴ 50

Postdoctoral Fellowship in Cancer Radiomics + Genomics @ Princess Margaret Cancer Centre (Toronto, Canada)

We seek a postdoctoral fellow for a project that aims at combining radiomics features extracted from radiological images CT and MRI scans) with genomics features (whole-genome and RNA-sequencing from the primary and sequencing of circulating tumor DNA) to better monitor treatment effect and predict therapy response. A combination of engineered features and deep learning models will be used to extract and jointly analyze the imaging and genomic data.

The first objective is to develop and evaluate novel computational imaging processing methods applied to multiple cancer types treated with chemotherapies, target therapies or immunotherapies. The second objective is to develop a cloud-based platform to facilitate the visualization and basic analysis of radiomics+genomics data in collaboration with the Software Engineers in the Haibe-Kains Laboratory (

This project (OCTANE 2.0) is funded by the Ontario Institute for Cancer Research and supported by Canexia Health. The candidate will be working in the Haibe-Kains Lab at the Princess Margaret Cancer Centre, University Health Network in collaboration with the Cancer Genomics Program and the Quantitative Imaging for Personalized Cancer Medicine, Techna Institute. A description of the project is available here.

Required qualifications Doctorate in Engineering, Physics, Bioinformatics, Computer Science, or related subject, with an interest in advanced image analysis, artificial intelligence, and machine learning. Expertise in Python, R and Unix programming environments.

Preferred qualifications Hands-on experience in high performance computing, especially for deep learning in Python (PyTorch Lightning) and genomic analysis in R in a cluster environment (Slurm). An understanding of image acquisition and reconstruction protocols and standardization would be helpful.

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 The subject line of your email should start with “POSTDOC RADIOMICS -- BHKLAB”. All documents should be provided in PDF.

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:

Lab directors Dr. Benjamin Haibe-Kains has over 10 years of experience in computational analysis of genomic and imaging data. He is the (co-)author of more than 200 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.

radiomics genomics UHN postdoc • 301 views

Login before adding your answer.

Traffic: 1118 users visited in the last hour
Help About
Access RSS

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6