University Health Network
Healthcare
PostdoctoralResearcher
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“Postdoctoral Researcher at University Health Network. Skills: AI/ML, Foundation models, Biomarker discovery, Drug discovery. Design multimodal drug foundation models. Integrate molecular graph representations”
What You'll Achieve.
Build general-purpose drug foundation model; Advance AI-driven drug discovery; Advance biomarker development; Advance clinical translation
Industry & Context.
Root cause analysis; Debugging; Data-driven decision making; Troubleshooting
What They're Looking For.
Must Have
5+ years Python experience, SQL proficiency, Statistics degree, Computer Science degree, Mathematics degree
Nice to Have
PhD preferred, GCP Professional Data Engineer, AWS Data Analytics, Databricks Certified, Dbt Certified, Experience with clinical data harmonization, Experience designing scalable bioinformatics pipelines, Understanding of regulatory and data governance requirements, Prior collaborative work across computational and clinical research teams
What You'll Do.
Design multimodal drug foundation models
Integrate molecular graph representations
Integrate transcriptomic perturbation signatures
Integrate multi-omics cell-state representations
Develop transfer learning strategies
Support diverse drug prediction tasks
Build flexible AI/ML workflows
Reduce reliance on task-specific architectures
Enable generalization across therapeutic contexts
Architect secure computational infrastructure
Deploy scalable computational infrastructure
Develop agentic AI-enabled frameworks
Support harmonization of clinical data
Support annotation of genomic data
Support integration of transcriptomic data
Implement distributed analysis pipelines
Enable multi-cohort biomarker assessment
Develop systematic workflows to evaluate signatures
Assess predictive value of molecular signatures
Integrate biomarker discovery pipelines
Integrate immunotherapy inference pipelines
Support translational studies
How You'll Work.
Team & Collaboration
Interdisciplinary teams; Computational partners; Clinical partners; Industry partners
Communication Scope
Executive presentations; Technical documentation
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: June 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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