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spatio-temporal generative models, and multimodal foundation models—including vision-language MLLMs and agentic AI frameworks—for longitudinal MRI and clinical data. Fellows will help build next-generation
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and observational measures of cognitive and emotional regulation (CER) for team settings that draw on rich multimodal data. (2) Develop a unified set of best practices for collecting and analyzing
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: Analyze fMRI data to investigate brain activity and connectivity. Model brain functional connectivity using static and dynamic approaches. Utilize Dynamic Causal Modeling (DCM) to study brain network
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analysis of PFAS. This task notably involves cutting-edge work on data processing. Simultaneously deploy targeted analysis quantification and non-targeted identification of PFAS in samples obtained from
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& Evidence: Investigating applied policy and programming questions to strengthen pandemic prevention, such as early warning systems, crisis data infrastructure, and overlooked drivers of vulnerability
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the resource constraints of the future electronics readout system. Collaborate closely with researchers at McGill (in both the Departments of Physics and in Computer and Electrical Engineering) and partners
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data collection and analysis for publication in peer-reviewed journals. The position will also include mentoring of undergraduate students. Qualifications: Research publications, grant writing
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are: Create favourable circumstances for the development of a field of research and a community of research on access to justice for OLMCs Produce scientific data for and with the access to justice stakeholders
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. Qualifications: A doctoral degree in epidemiology Knowledge of advanced epidemiologic methods Strong data manipulation and analytic skills using statistical programming software, e.g., SAS Demonstrated ability
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participating in the collaborative development of: (1) spatially explicit baseline data of cropping practices, including identification of representative crop rotations, and (2) spatially explicit baseline