Research Technical Assistant - AI and Multimodal Foundation Models

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.

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