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Benjamin Haibe Kains

Professor

University Health Network

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United States

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Research Interests

Statistics

10%

Artificial Intelligence

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Computational Biology

10%

Medical Science

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Biology

10%

Multi-task Learning

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Machine Learning

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Positions1

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University Health Network

University Health Network

Postdoctoral Researcher in AI, Machine Learning, and Computational Biology at Princess Margaret Cancer Centre

University Health Network (UHN), through the Benjamin Haibe Kains Lab at Princess Margaret Cancer Centre in Toronto, is recruiting a Postdoctoral Researcher in AI, Machine Learning, and Computational Biology . This opportunity is aimed at a highly motivated researcher interested in next-generation methods for drug discovery , precision oncology , and translational cancer research . The lab’s work spans several advanced research directions, including AI foundation models for drug discovery and repurposing, multimodal learning that integrates molecular, transcriptomic, and clinical data, biomarker discovery across chemotherapy, targeted therapy, and immunotherapy, secure and privacy-preserving distributed analytics for multi-institutional research, and agentic AI plus computational infrastructure for large-scale biomedical data integration. The successful candidate will work in an interdisciplinary environment and collaborate with computational scientists, clinicians, and industry partners. The post is based in one of the world’s leading cancer research settings, offering exposure to high-impact biomedical research and real-world translational applications. Eligibility highlights: postdoctoral-level researcher with strong interest in AI/ML and computational biology; experience relevant to biomedical data, cancer research, or large-scale analytics would be advantageous, although no formal requirements are listed in the post. Funding: the post does not specify stipend, salary, or duration details in the announcement. How to apply: interested candidates should apply through the UHN Careers website. The post also invites readers to share the opportunity with suitable candidates.