Research Technical Assistant – AI, Multimodal Foundation Models

This is a fully remote position, open to applicants in Canada.

📋 Description

• Support the development and evaluation of foundation models and multimodal AI techniques tailored for biomedical and healthcare applications.

• Aid in the design and execution of AI workflows, both single- and multi-agent, focusing on reasoning, information integration, and decision support.

• Collaborate with multimodal biomedical and clinical datasets, encompassing genomics, electronic health records, clinical narratives, molecular data, as well as structured and unstructured health information.

• Assist in the integration of longitudinal clinical and genomic data into cohesive AI representations.

• Design, test, and refine machine-learning and deep-learning algorithms utilizing Python, PyTorch, and associated frameworks.

• Engage in data preprocessing, representation learning, model training, benchmarking, and assessment.

• Contribute to research initiatives involving foundation-model adaptation, agent memory, reasoning, and multimodal learning within the healthcare sector.

• Perform literature reviews and stay updated on advancements in foundation models, large language models, multimodal AI, and autonomous or multi-agent AI systems.

• Assist in conducting research experiments, analyzing results, preparing figures, and interpreting model performance.

• Participate in writing manuscripts, submitting to conferences, delivering presentations, and producing technical reports, as well as contributing to open-source research software.

• Adhere to best practices in software engineering and reproducible research, including version control, documentation, experiment tracking, and reproducible computational workflows.

• Collaborate with AI researchers, scientists, clinicians, and domain specialists on interdisciplinary biomedical research endeavors.

• Attend research meetings, engage in project discussions, and partake in regular progress evaluations.

⛳️ Requirements

• Currently pursuing an undergraduate, master's, or doctoral degree in Computer Science, Biomedical Engineering, Computational Biology, Bioinformatics, Data Science, Artificial Intelligence, Statistics, Applied Mathematics, or a related quantitative field.

• Proficient programming skills in Python and experience with scientific computing or machine-learning workflows.

• Familiarity with deep learning frameworks, particularly PyTorch.

• Solid grasp of machine-learning and deep-learning principles.

• Knowledge of foundation models, large language models, multimodal AI, or agentic AI frameworks like LangChain or LangGraph is an advantage.

• Keen interest in applying AI techniques to biomedical, genomic, and clinical datasets.

• Capability to work both independently and collaboratively within an interdisciplinary research setting.

• Ability to juggle multiple responsibilities, quickly adapt to new methods, and contribute effectively in a dynamic and translational research environment.

• Strong analytical, problem-solving, communication, and organizational skills.

• Familiarity with distributed training and high-performance computing environments such as SLURM is a plus.

• Experience with Linux, Git, high-performance computing, cloud computing, or reproducible research practices is beneficial.

• A Criminal Record Check may be necessary for the selected candidate.

🏝️ Benefits

• Government organization and participant in the Healthcare of Ontario Pension Plan (HOOPP).

• Convenient access to public transit and UHN shuttle service.

• Flexible working environment.

• Opportunities for growth and advancement within a large organization.

• Corporate discounts on travel, dining, parking, mobile plans, and auto insurance.

• On-site fitness facilities.