Job Description

Company Description

UHN is Canada’s #1 hospital and the world’s #1 publicly funded hospital. With 10 sites and more than 44,000 TeamUHN members, UHN consists of Toronto General Hospital, Toronto Western Hospital, Princess Margaret Cancer Centre, Toronto Rehabilitation Institute, The Michener Institute of Education and West Park Healthcare Centre. As Canada's top research hospital, the scope of biomedical research and complexity of cases at UHN have made it a national and international source for discovery, education and patient care. UHN has the largest hospital-based research program in Canada, with major research in neurosciences, cardiology, transplantation, oncology, surgical innovation, infectious diseases, genomic medicine and rehabilitation medicine. UHN is a research hospital affiliated with the University of Toronto.

UHN’s vision is to build A Healthier World and it’s only because of the talented and dedicated people who work here that we are continually bringing that vision closer to reality.

www.uhn.ca

Job Description

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, 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

• 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.

Additional Information

​​​​​Why join UHN?

In addition to working alongside some of the most talented and inspiring healthcare professionals in the world, UHN offers a wide range of benefits, programs and perks. It is the comprehensiveness of these offerings that makes it a differentiating factor, allowing you to find value where it matters most to you, now and throughout your career at UHN.

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

Current UHN employees must have successfully completed their probationary period, have a good employee record along with satisfactory attendance in accordance with UHN's attendance management program, to be eligible for consideration.

All applications must be submitted before the posting close date.

UHN uses email to communicate with selected candidates. Please ensure you check your email regularly. At University Health Network (UHN), artificial intelligence technologies may be used to assist in the screening, assessment, and selection of candidates for this position.

Please be advised that a Criminal Record Check may be required of the successful candidate. Should it be determined that any information provided by a candidate be misleading, inaccurate or incorrect, UHN reserves the right to discontinue with the consideration of their application.

UHN is an equal opportunity employer committed to an inclusive recruitment process and workplace. Requests for accommodation can be made at any stage of the recruitment process. Applicants need to make their requirements known.

We thank all applicants for their interest, however, only those selected for further consideration will be contacted.