Research Technical Assistant - AI and Multimodal Foundation Models

UHN is Canada’s #1 hospital and the world’s #1 publicly funded hospital. With 10 sites and more than 44,000

TeamUHNmembers, 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, genomicmedicineand rehabilitation medicine.

UHN is a research hospital affiliated with the University of Toronto.

UHN’s vision is to build A Healthier Worldandit’sonly because of the talented and dedicated people who work here that we are continually bringing that vision closer to reality.

Union
:
Non-Union
Number of vacancies
: 1
New or Replacement Position
:
New
Site
:

Toronto General Hospital
Department
:

Peter Munk Cardiac Centre (PMCC)
Reports to
:
Principal Investigator
Salary Range
: $22.60 - $28.25 per hour
Hours
:20 hours per week
Shifts
:
Days
Status
:
Temporary Part-time (Approximately 3 months to start)
Closing Date
:
September 15, 2026

The Peter Munk Cardiac Centre (PMCC) AI team is seeking one highly motivated AI Research Student to join our multidisciplinary team and contribute to the development of next-generation multimodal foundation models and agentic AI systems for biomedical and healthcare applications. This is a unique chance to be at the cutting edge of AI research in healthcare, driving projects that make a tangible impact on patient outcomes and clinical practices around the globe.

Working closely with the Lead AI Scientist, staff scientists, clinicians, and domain experts, students will participate in cutting-edge research aimed at building AI systems capable of integrating diverse biological modalities and enabling translational discoveries. This position offers a unique opportunity to work at the intersection of computer vision, natural language processing, structural biology, and biomedical imaging. Successful candidates will have the opportunity to contribute to high-impact research projects, publications, and open‑source software development.

This position provides an exceptional opportunity to gain hands‑on experience in state‑of‑the‑art AI research and collaborate with leading scientists in a highly interdisciplinary environment.

• Assist in the development and evaluation of foundation models and multimodal AI methods for biomedical and healthcare applications.
• Contribute to the design and implementation of agentic AI systems, including single- and multi-agent workflows for reasoning, information integration, and decision support.
• Work with multimodal biomedical and clinical data, with a primary focus on genomics, electronic health records, clinical text, molecular data, and other structured and unstructured health data.
• Assist with the development and evaluation of methods for integrating longitudinal clinical and genomic information into unified AI representations.
• Develop, test, and optimize machine-learning and deep-learning methods using Python, PyTorch, and related frameworks.
• Participate in data preprocessing, representation learning, model training, benchmarking, and evaluation of AI models.
• Contribute to research on foundation-model adaptation, agent memory, reasoning, and multimodal learning for healthcare applications.
• Conduct literature reviews and remain current with developments in foundation models, large language models, multimodal AI, and autonomous/multi-agent…