University Health Network
Healthcare
PostdoctoralResearcher
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“Postdoctoral Researcher at University Health Network. Skills: AI/ML, foundation models, deep learning, transfer learning, Python, PyTorch, Hugging Face, biomedical data. Design and implement multimodal drug foundation models that integrate molecular graph representations, bulk transcriptomic perturbation signatures, and multi-omics cell-state representations. Develop transfer learning strategies to support diverse drug prediction tasks”
What You'll Achieve.
build a general-purpose drug foundation model capable of transfer learning across diverse prediction tasks; develop and apply secure, scalable, and privacy-preserving computational infrastructure to support biomarker discovery across diverse treatment modalities; advance AI-driven drug discovery, biomarker development, and clinical translation
Industry & Context.
Criminal Record Check may be required
What They're Looking For.
Must Have
PhD within the previous 5 years, or an MD or DDS within the previous 10 years in a relevant quantitative or biomedical discipline, Demonstrated experience developing or applying 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, programming skills in Python and/or R, practical experience using modern machine learning frameworks and tooling, Experience working with large-scale biomedical datasets, Proficiency with reproducible workflow management systems, Familiarity with cloud or high-performance computing environments
Nice to Have
Understanding of data harmonization, privacy-preserving analysis, federated learning, secure distributed computing, or clinical data governance, publication record, commensurate with career stage, in computational biology, AI/ML, bioinformatics, biostatistics, biomedical data science, or related fields, Experience with clinical data harmonization (e. g. , OMOP CDM, HL7 FHIR), 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
What You'll Do.
Design and implement multimodal drug foundation models that integrate molecular graph representations
bulk transcriptomic perturbation signatures
and multi-omics cell-state representations
Develop transfer learning strategies to support diverse drug prediction tasks
Build flexible AI/ML workflows that reduce reliance on task-specific architectures and enable generalization across therapeutic contexts
and treatment modalities
Architect and deploy secure
and privacy-preserving computational infrastructure for biomarker discovery and translational cancer research
Develop agentic AI-enabled frameworks to support the harmonization
and integration of clinical
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
Assess the predictive value of molecular signatures across cancer types and treatment modalities
Integrate biomarker discovery and immunotherapy inference pipelines with clinical data warehouses to support translational studies
Contribute to responsible data sharing frameworks
data governance processes
and regulatory documentation as required
How You'll Work.
Team & Collaboration
Collaborate closely with computational biologists, software developers, clinicians, and wet-lab scientists to build, validate, and translate predictive models and biomarker discovery workflows; work collaboratively in interdisciplinary teams spanning computational biology, machine learning, software engineering, oncology, and clinical research
Communication Scope
Excellent communication skills
Full Job 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](https://www.uhn.ca/) Union: Non-Union Number of Vacancies: 1 New or Replacement Position: New Site: MaRS Department: PM Research Reports to: Senior Scientist Salary Range: $54,902 - $93,333 Per Year Hours: 37.5 Hours Per Week Shifts: Monday - Friday; Day Shifts Status: Temporary Full-time (2-Year Contract) Closing Date: May 26, 2026 Position Summary: We are seeking a postdoctoral researcher with strong expertise in AI/ML to join a major interdisciplinary initiative focused on developing foundation models and secure computational infrastructure for translational cancer research. The first major objective is to build a general-purpose drug foundation model capable of transfer learning across diverse prediction tasks, including mechanism of action classification, clinical drug response prediction in tumour subtypes, ADMET and toxicity profiling, combinatorial drug synergy, and drug repurposing. The goal is to move beyond task-specific architectures and datasets toward flexible
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