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 models that can generalize across therapeutic contexts.

The second major objective is to develop and apply secure, scalable, and privacy-preserving computational infrastructure to support biomarker discovery across diverse treatment modalities. This will include building agentic AI approaches to harmonize clinical, genomic, and transcriptomic data across public and private cohorts, while assessing the predictive value of DNA and RNA signatures in federated settings where sensitive data remain under local governance.

Together, these efforts aim to advance AI-driven drug discovery, biomarker development, and clinical translation by integrating modern machine learning, multimodal biomedical data, and robust distributed analysis frameworks. The successful candidate will work in the Haibe-Kains Lab at the Princess Margaret Cancer Centre, University Health Network.

Duties:

• 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, including mechanism of action classification, clinical drug response prediction, ADMET and toxicity profiling, combinatorial drug synergy, and drug repurposing.
• 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.