Postdoctoral Researcher

Union:  Non-Union Number of Vacancies:  1 New or Replacement Position: New Site:  MaRS Department:  Research Reports to:  Senior Scientist Salary Range: $54,902 - $82,352 annually Hours:  37.5 Hours Per Week Shifts:  Monday - Friday; Day Shifts Status:  Temporary Full-time (2-Year Contract) Closing Date:  November 26, 2026  Position Summary We seek a postdoctoral fellow with strong expertise in computational biology, bioinformatics, biomedical data science, and/or AI/ML to join a major interdisciplinary initiative focused on developing computational approaches for translational cancer research. The ideal candidate will have a strong quantitative or computational background together with demonstrated experience in AI applied to genomic, transcriptomic, multi-omics, molecular, pharmacological, or clinical data. The position is particularly suited to candidates interested in addressing biologically and clinically meaningful questions at the intersection of computational oncology, cancer genomics, machine learning, biomarker discovery, and scientific AI. A major focus of the position will be computational biomarker discovery and therapeutic response modeling across cancer types and treatment modalities. This will include identifying predictive and response-associated molecular signatures, characterizing disease- and treatment-related biological mechanisms, prioritizing potential therapeutic targets, and developing computational models of drug and treatment response. Methodological priorities will include multimodal and representation learning, as well as foundation-model approaches that integrate multiple biological and clinical data layers to improve prediction, generalization, and biological interpretation. A second major focus will be the development of scalable, reproducible, and privacy-aware computational frameworks for multi-cohort translational studies. This will include agentic AI-enabled approaches for data harmonization, annotation, quality control, integration, analysis, and scientific interpretation across public and institutional datasets, as well as distributed and federated approaches in settings where sensitive data remain under local governance. Together, these efforts aim to advance computational oncology, biomarker development, therapeutic discovery, and precision medicine by combining modern machine learning with rigorous computational biology, multimodal biomedical data integration, and reproducible distributed analysis.  The candidate will be working in the Haibe-Kains Lab at the Princess Margaret Cancer Centre , University Health Network , within a multi-institutional research collaboration involving the Rosalind & Morris Goodman Cancer Institute at McGill University, as well as other academic, clinical, and industry partners. What We Offer • A highly collaborative, interdisciplinary research environment spanning computational biology, oncology, clinical informatics, and AI • Access to large-scale proprietary clinical datasets (tumour biopsies, treatment outcomes, longitudinal genomics) alongside major public resources • State-of-the-art GPU cluster and cloud computing infrastructure with dedicated allocations • Mentorship from senior faculty with strong track records in Nature/Science-family publications and competitive grant funding • Opportunities to collaborate with clinical teams, pharmaceutical industry partners, and international research consortia • Support for conference travel, professional development, and independent fellowship applications Duties • Design and implement multimodal computational and foundation models integrating molecular, genomic, transcriptomic, multi-omics, treatment, and clinical data.  • Develop transfer learning strategies to therapeutic prediction tasks, inc