Postdoctoral Researcher; Machine Learning Multimodal Healthcare AI

Postdoctoral Researcher (Machine Learning for Multimodal Healthcare AI)

We are seeking a Postdoctoral Researcher in Machine Learning / Computer Science to help build the next frontier of deep learning, multimodal fusion, and longitudinal modeling in clinical medicine. The role focuses on transplant medicine and liver disease and will be co‑supervised by Dr. Mamatha Bhat (clinician) and Dr. Divya Sharma (Computer Science).

• Build generative and predictive models using longitudinal, multimodal patient data including clinical variables, labs, imaging, pathology, and multi‑omics.
• Design and deploy foundation‑model‑inspired architectures for real‑time clinical applications.
• Incorporate causal inference and counterfactual modeling to guide treatment simulations and improve decision‑making.
• Develop clinician‑facing software tools that embed ML models into UHN’s digital ecosystem.
• Contribute to high‑impact research publications, funding proposals, and collaborative innovations across AI and medicine.
• Integrate and preprocess large‑scale longitudinal datasets from diverse sources such as EHRs, biobanks, and imaging repositories.
• Train and validate predictive and generative models leveraging deep learning, causal inference, and time‑aware architectures.
• Translate research outputs into clinician‑facing software applications with real‑time capabilities.
• Collaborate with clinicians, data scientists, and health system partners to lead hypothesis‑driven translational research.
• Document technical workflows, model development pipelines, and reproducibility procedures.
• Support junior trainees, including students and analysts, through mentorship and collaboration.
• Participate in regular joint meetings and milestone planning with supervisors.

• A recent or soon‑to‑be PhD graduate in Machine Learning, Computer Science, Bioinformatics, or related fields.
• Proficiency in Python and experience with deep learning frameworks such as PyTorch or Tensor Flow.
• Familiarity with or enthusiasm for deep generative models, causal machine learning, transformer architectures, foundational models, and multimodal learning.
• A collaborative and curious researcher with a strong publication record, excellent communication skills, and a passion for translational AI in medicine.

• Competitive salary package.
• Participation in the Healthcare of Ontario Pension Plan.
• Access to UHN shuttle service and convenient transit.
• Flexible working environment.
• Opportunities for professional development and promotion.
• Corporate discounts on travel, restaurants, parking, phone plans, auto insurance, on‑site gym, and more.

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.