Posted: June 27, 2026
Postdoctoral Researcher - University Health Network, Toronto is listed at University Health Network in Toronto, ON. The Beaver Board aggregates this opening from the employer's career page — apply using the button below to reach the official application. Browse more jobs at University Health Network.
The University Health Network (UHN) in Toronto, ON, is seeking a Postdoctoral Researcher with strong expertise in AI/ML. This role focuses on developing foundation models and secure computational infrastructure for translational cancer research, including AI-driven drug discovery and biomarker development. Key duties involve designing multimodal drug foundation models and architecting privacy-preserving computational infrastructure. Top requirements include a PhD in a relevant quantitative or biomedical field, strong deep learning experience, and proficiency in Python or R.
The University Health Network (UHN) is at the forefront of innovative cancer research, and this Postdoctoral Researcher position plays a pivotal role in a major interdisciplinary initiative. The primary objective is to construct a general-purpose drug foundation model, leveraging transfer learning techniques to excel across diverse prediction tasks. This includes mechanism of action classification, clinical drug response prediction in tumour subtypes, ADMET and toxicity profiling, combinatorial drug synergy, and drug repurposing. The aim is to move beyond task-specific architectures, creating flexible models that can generalize across various therapeutic contexts.
Furthermore, the role involves developing and applying secure, scalable, and privacy-preserving computational infrastructure to support biomarker discovery. This includes building agentic AI approaches to harmonize clinical, genomic, and transcriptomic data across public and private cohorts, assessing the predictive value of DNA and RNA signatures in federated settings where sensitive data remain under local governance. This crucial research will integrate modern machine learning with multimodal biomedical data and robust distributed analysis frameworks to advance AI-driven drug discovery and clinical translation.
Candidates for this Postdoctoral Researcher position must possess a PhD awarded within the previous five years, or an MD/DDS within ten years, in a quantitative or biomedical discipline such as Computational Biology, Machine Learning, or Data Science. Demonstrated experience developing or applying deep learning methods to molecular, biological, clinical, or multi-omics data is essential. Expertise in modern AI/ML approaches relevant to foundation models or representation learning, including graph neural networks or transfer learning, is critical. Strong programming skills in Python and/or R, coupled with practical experience using frameworks like PyTorch, are required.
The successful candidate will work extensively with large-scale biomedical datasets, including molecular graphs and multi-omics data, and should be proficient with reproducible workflow management systems. Familiarity with cloud or high-performance computational environments and an understanding of data harmonization, privacy-preserving analysis, or federated learning are highly desirable. This role demands excellent communication skills for collaborative work across computational biologists, software developers, clinicians, and wet-lab scientists, contributing to responsible data sharing frameworks and ethical research at UHN.
