Research Technical Assistant - AI and Multimodal Foundation Models

UHN Research; Toronto; Hybrid; CAD 31,000 - 39,000; Part time

Benefits offered by this job

Competitive offer packages

HOOPP pension

Transit access

Flexible work environment

Development opportunities

Corporate discounts

Job summary

UHN is seeking an AI research student to join the PMCC AI team at Toronto General Hospital. The role focuses on developing next‑generation multimodal foundation models for biomedical and healthcare applications, with a strong emphasis on integrating diverse data types and translational impact.

Working with the Lead AI Scientist and clinicians, you will contribute to cutting-edge research in computer vision, NLP, and biomedical imaging, while gaining hands-on experience in state‑of‑the‑art AI

Qualifications

• Currently enrolled in an undergraduate, master's, or doctoral program in a quantitative field.
• Strong programming skills in Python and experience with ML workflows.
• Experience with PyTorch and scientific computing stacks.
• Familiarity with foundation models, LLMs, or multimodal AI is an asset.
• Solid understanding of machine-learning fundamentals.

Responsibilities

• Assist in developing and evaluating foundation models for biomedical AI.
• Contribute to design of agentic AI systems and multi-agent workflows.
• Work with genomics, EHR, clinical text, and molecular data.
• Develop, test, and optimize ML/DL methods using Python and PyTorch.
• Prepare figures, interpret model performance, and contribute to reports.
• Follow reproducible research practices and collaborate across disciplines.

Skills

Python

PyTorch

LangChain

LangGraph

Education

Undergrad/Grad studies in CS or quantitative field

Tools

PyTorch

NumPy/SciPy

Git

Linux

Job description

Research Technical Assistant - AI and Multimodal Foundation Models

• Part-time
• Department: Research
• Compensation: CAD 22.60 - CAD 28.25 - hourly

UHN is Canada’s #1 hospital and the world’s #1 publicly funded hospital. With 10 sites and more than 44,000TeamUHNmembers, 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,educationand 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 HealthierWorldandit’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
SalaryRange: $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

Position Summary

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.

The successful candidate will join the highly collaborative and exceptionally productive PMCC AI team, which brings together clinicians, AI and staff scientists, software developers, machine learning engineers, researchers, and operational leaders across cardiology, cardiac surgery, vascular surgery, critical care, medical imaging, and digital health, and will help ensure that AI tools are safe, auditable, scalable, and aligned with the strategic objectives of the PMCC and UHN. and aligned with the strategic objectives of the PMCC and UHN., and aligned with the strategic objectives of the PMCC and UHN.For more information about the PMCC AI Team, please visit our website at https://pmcc.ai .

Duties

• 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 AI systems.
• Assist with research experiments, analysis of results, preparation of figures, and interpretation of model performance.
• Contribute to research manuscripts, conference submissions, presentations, technical reports, and open-source research software.
• Follow software-engineering and reproducible-research best practices, including version control, documentation, experiment tracking, and reproducible computational workflows.
• Collaborate with AI researchers, staff scientists, computational scientists, clinicians, and other domain experts on interdisciplinary biomedical research projects.
• Participate actively in research meetings, project discussions, and regular progress reviews.
• Currently enrolled in an undergraduate, master's, or doctoral program in Computer Science, Biomedical Engineering, Computational Biology, Bioinformatics, Data Science, Artificial Intelligence, Statistics, Applied Mathematics, or a related quantitative discipline.
• Strong programming skills in Python and experience with scientific computing or machine-learning workflows.
• Experience with deep learning frameworks such as PyTorch.
• Familiarity with foundation models, large language models, multimodal AI, or agentic AI frameworks such as LangChain or LangGraph is an asset.
• Solid understanding of machine-learning and deep-learning fundamentals

Preferred Qualifications

• Interest in applying AI methods to biomedical, genomic, and clinical data.
• Ability to work both independently and collaboratively in an interdisciplinary research environment.
• Ability to manage multiple tasks, learn new methods quickly, and contribute to a dynamic and translational research setting.
• Familiarity with distributed training and high-performance computing environments such as SLURM will be an asset.
• Strong analytical, problem-solving, communication, and organizational skills.
• Experience with Linux, Git, high-performance computing, cloud computing, or reproducible research workflows is an asset.

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 https://hoopp.com/)
• 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.

UHN uses email to communicate with selected candidates. Please ensure you check your email regularly.

At University Health Network (UHN), artificial intelligence technologies may be used to assist in the screening, assessment, and selection of candidates for this position.

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.