Research Technical Assistant - AI and Multimodal Foundation Models
About The Position
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.
Requirements
- 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.
Nice To Haves
- 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.
Responsibilities
- 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.
Benefits
- 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.)
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
No Education Listed
