Postdoctoral Fellow in Machine Learning for Multimodal Healthcare AI

University Health Network

  • Toronto, ON
  • Temporary
  • Full-time
  • 8 days ago
Company DescriptionUHN is Canada's #1 hospital and the world's #1 publicly funded hospital. With 10 sites and more than 44,000 Team UHN 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.Job DescriptionUnion: Non-Union
Number of Vacancies: 1
Site: Toronto General Hospital Research
Department: Multi organ Transplant
Reports to: Dr. Mamatha Bhat
Hours: 37.5
Shifts: Monday to Friday
Status: Temporary Full-Time
Closing Date: October 31, 2025Position SummaryWe're seeking a Postdoctoral Research Fellow 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.
Duties1. 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).
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
Qualifications
  • A recent (or soon-to-be) PhD graduate in Machine Learning, Computer Science, Bioinformatics, or related fields.
  • Fluent in Python, and experienced with deep learning frameworks like PyTorch or TensorFlow.
  • Familiar with (or excited to learn) deep generative models, causal ML, 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.
Additional InformationWhy join UHN?In addition to working alongside some of the most talented and inspiring healthcare professionals in the world, UHN offers a wide range of benefits, programs and perks. It is the comprehensiveness of these offerings that makes it a differentiating factor, allowing you to find value where it matters most to you, now and throughout your career at UHN.
  • Competitive offer packages
  • Government organization and a member of the Healthcare of Ontario Pension Plan (HOOPP
) * Close access to Transit and UHN shuttle service
  • A flexible work environment
  • Opportunities for development and promotions within a large organization
  • Additional perks (multiple corporate discounts including: travel, restaurants, parking, phone plans, auto insurance discounts, on-site gyms, etc.)
Current UHN employees must have successfully completed their probationary period, have a good employee record along with satisfactory attendance in accordance with UHN's attendance management program, to be eligible for consideration.All applications must be submitted before the posting close date.UHN uses email to communicate with selected candidates. Please ensure you check your email regularly.Please be advised that a Criminal Record Check may be required of the successful candidate. Should it be determined that any information provided by a candidate be misleading, inaccurate or incorrect, UHN reserves the right to discontinue with the consideration of their application.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. Applicants need to make their requirements known.We thank all applicants for their interest, however, only those selected for further consideration will be contacted.

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