*REPOST*

Union: Non-Union
Number of Vacancies: 1
New or Replacement: New
Site: Toronto General Hospital Research
Department: Multi organ Transplant
Reports to: Dr. Mamatha Bhat
Hours: 37.5
Salary: $54,902 to $93,333 per year
Shifts: Monday to Friday
Status: Temporary Full-Time
Closing Date: June 30, 2026

Position Summary

We're 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. This unique position offers the opportunity to work at the intersection of AI, healthcare, and translational science tackling some of the most complex challenges in transplant medicine and liver disease. The candidate would be co-supervised by both 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 your ML models into UHN’s digital ecosystem.
• Contribute to high-impact research publications, funding proposals, and collaborative innovations across AI and medicine.
• innovations across AI and medicine.

Duties

• Data Integration & Preprocessing
• Preprocess and harmonize large-scale longitudinal datasets comprising structured (clinical/lab) and unstructured (imaging, pathology, molecular) data.
• Develop reproducible pipelines for multimodal data ingestion from diverse health system and research sources (e.g., EHRs, biobanks, imaging repositories).
• Machine Learning & Model Development
• Design, train, and validate predictive and generative models leveraging deep learning, causal inference, and time-aware architectures.
• Build foundation model-inspired pipelines for patient trajectory modeling, treatment response simulation, and risk stratification in liver disease and transplantation.
• Translational AI Tool Deployment
• Translate research outputs into clinician-facing software applications, ensuring integration into UHN’s digital ecosystem.
• Build user-friendly, interpretable tools with real-time capability to support decision-making in complex clinical workflows.
• Scientific Discovery & Collaboration
• Co-lead hypothesis-driven, translational research in collaboration with clinicians, data scientists, and health system partners.
• Explore novel computational strategies for multimodal fusion and disease modeling.
• Knowledge Mobilization & Scholarly Output
• Contribute to high-impact publications, presentations, and grant proposals that bridge AI and healthcare.
• Document technical workflows and model development for reproducibility and knowledge sharing.
• Supervision & Mentorship
• Engage with and support junior trainees, including students and analysts, contributing to shared project goals and team culture.
• Collaborate closely with supervisors Dr. Mamatha Bhat and Dr. Divya Sharma through regular joint meetings and milestone planning.
• Learning & Growth
• Stay current on state-of-the-art developments in machine learning, generative modeling, and precision medicine.
• Adapt models and methods to evolving project requirements in a fast-paced, interdisciplinary environment.