Postdoctoral Researcher - University Health Network, Toronto

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 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 You'll Do

As a Postdoctoral Researcher contributing to University Health Network jobs in Toronto, you will undertake key duties involving computational oncology, research, and machine learning:

• 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, including drug response, mechanism of action, synergy, and repurposing.
• Build secure, scalable, and privacy-preserving AI/ML workflows for biomarker discovery and translational research, aligned with responsible data sharing, governance, ethics, and regulatory requirements.
• Build flexible AI/ML workflows that reduce reliance on task-specific architectures and enable generalization across therapeutic contexts, tumour subtypes, and treatment modalities.
• Architect and deploy secure, scalable, and privacy-preserving computational infrastructure for biomarker discovery and translational cancer research.
• Develop agentic AI-enabled frameworks to support the harmonization, annotation, quality control, and integration of clinical, genomic, and transcriptomic data across public cohorts and private institutional datasets.
• Implement distributed and federated analysis pipelines in which each contributing dataset can be analysed separately, enabling multi-cohort biomarker assessment without raw data centralization.
• Develop systematic workflows to evaluate published and user-specified DNA and RNA signatures, including immune, stromal, mutation-based, pathway-level, and treatment-response signatures.
• Assess the predictive value of molecular signatures across cancer types and treatment modalities, including chemotherapy, targeted therapy, immunotherapy, and emerging therapeutic approaches.
• Integrate biomarker discovery and immunotherapy inference pipelines with clinical data warehouses to support translational studies in collaboration with clinical, industry, and computational partners.
• Contribute to responsible data sharing frameworks, data governance processes, IRB/ethics protocols, and regulatory documentation as required.
• Collaborate closely with computational biologists, software developers, clinicians, and wet-lab scientists to build, validate, and translate predictive models and biomarker discovery workflows.

Qualifications

Candidates interested in these healthcare jobs in Toronto must meet the following educational and technical requirements:

• PhD completed, or expected within 3 months of submission, in a relevant quantitative or biomedical discipline, including but not limited to Computational Biology, Bioinformatics, Systems Biology, and/or Quantitative Genomics; Machine Learning, Artificial Intelligence, Computer Science, and/or Data Science; Biostatistics, Biomedical Engineering, or related fields.
• Demonstrated experience developing or applying computational, statistical, machine-learning, or deep-learning methods to molecular, biological, clinical, or multi-omics data.
• Expertise in one or more modern AI/ML approaches relevant to foundation models or representation learning, such as graph neural networks, transformers, generative models, self-supervised learning, or transfer learning.
• Strong programming skills in Python and/or R, with practical experience using modern machine learning frameworks and tooling such as PyTorch, Hugging Face, PyTorch Geometric, Deep Graph Library, scikit-learn, or equivalent platforms.
• Experience working with large-scale biomedical datasets, such as molecular graphs, transcriptomic perturbation profiles, multi-omics data, clinical genomics, electronic health record-derived data, or treatment-response datasets.
• Familiarity with agentic AI, tool-using AI systems, data harmonization, federated learning, privacy-preserving analysis, or secure distributed computing is highly desirable.
• Proficiency with reproducible workflow management systems such as Snakemake, Nextflow, CWL, or equivalent pipeline frameworks.
• Familiarity with cloud or high-performance computing environments, such as GCP, AWS, SLURM-based clusters, or equivalent infrastructure.
• Understanding of data harmonization, privacy-preserving analysis, federated learning, secure distributed computing, or clinical data governance is highly desirable.
• Strong publication record, commensurate with career stage, in computational biology, AI/ML, bioinformatics, biostatistics, biomedical data science, or related fields.
• Excellent communication skills and ability to work collaboratively in interdisciplinary teams spanning computational biology, machine learning, software engineering, oncology, and clinical research.
• Experience with clinical data harmonisation (e.g., OMOP CDM, HL7 FHIR) is an advantage.
• Experience designing scalable bioinformatics pipelines for large-scale genomic or transcriptomic datasets.
• Understanding of regulatory and data governance requirements in clinical research settings.
• Prior collaborative work across computational and clinical or wet-lab research teams.

Why You'll Love It Here

Exploring University Health Network careers grants you access to a highly collaborative, interdisciplinary research environment and comprehensive employee offerings:

• 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.
• Competitive offer packages.
• Government organization and a member of the Healthcare of Ontario Pension Plan (HOOPP).
• Close access to Transit and UHN shuttle service.
• A flexible work environment.
• Opportunities for development and promotions within a large organization.
• Additional perks including multiple corporate discounts (travel, restaurants, parking, phone plans, auto insurance discounts, on-site gyms, etc.).