Research Technical Assistant - AI and Multimodal Foundation Models

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

Company Description

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

www.uhn.ca  

Job Description

Union: Non-Union
Number of vacancies: 2
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 hourly
Hours: 37.5 hours per week
Shifts: Days
Status: Temporary Full-time (Approximately 3 months to start)
Closing Date: July 7, 2026

Position Summary

The Peter Munk Cardiac Centre (PMCC) AI team is seeking two highly motivated AI Research Students 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 Chief 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 data 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 multimodal foundation models for biomedical applications.  
  • Contribute to the design and implementation of agentic AI systems capable of reasoning across diverse data modalities.  
  • Work with large-scale multimodal biomedical datasets, including CryoEM, CryoET, digital pathology, molecular profiling, and structural biology data.  
  • Develop and optimize machine learning and deep learning algorithms using Python, PyTorch, and related frameworks.  
  • Participate in data preprocessing, representation learning, model training, and benchmarking.  
  • Collaborate with researchers, clinicians, and domain experts to address translational challenges in cardiovascular medicine and precision health.  
  • Conduct literature reviews and remain up to date with emerging developments in foundation models, generative AI, and autonomous AI agents.  
  • Assist in preparing research manuscripts, conference presentations, technical reports, and open-source software releases.  
  • Support software engineering with best practices, including version control, reproducibility, and documentation.  
  • Participate actively in team meetings and interdisciplinary research discussions. 

Qualifications

  • Currently enrolled in an undergraduate, master's, or doctoral program in Computer Science, Medical Biophysics, Biomedical Engineering, Computational Biology, Data Science, Artificial Intelligence, or a related discipline.  
  • Ability to manage multiple projects and contribute to a dynamic and translational research environment.  
  • Strong programming skills in Python.  
  • Experience with deep learning frameworks (e.g., PyTorch) and familiarity with agentic AI frameworks (e.g., LangChain or LangGraph). 
  • Solid understanding of machine learning and deep learning fundamentals.  
  • Strong analytical, communication, and problem-solving skills.  

Preferred Qualifications  

  • Experience with large multimodal biomedical data, vision-language models, multimodal learning, generative AI, or agentic AI systems.  
  • Experience with computer vision, representation of learning, self-supervised learning, or foundation models.  
  • Familiarity with biological imaging modalities such as digital pathology, CryoEM, CryoET, microscopy, or structural biology datasets.   
  • Experience with protein modeling, molecular biology, or computational structural biology is considered an asset.  
  • Familiarity with distributed training, high-performance SLURM-based computing environments.   
  • Experience with Git, Docker, and modern software development practices.   
  • Prior research experience and publications are assets but are not required. 

Additional Information

Why 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 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.

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. 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.

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