17 power-systems-engineering-"https:"-"https:"-"https:" Postdoctoral positions at McGill University
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Learning (SCSIL) is seeking a highly motivated Postdoctoral Researcher with training in computer science, artificial intelligence, biomedical engineering, or a closely related field to join an
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McGill University | North Peel Regional Municipality Kleinburg, Ontario | Canada | about 1 month ago
of health and medicine This position is unionized, and the incumbent will hold dual status as both a student and a McGill employee, and as a member of the AMURE (Postdocs) bargaining unit. For interested
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of the following would be considered an asset: LLM-based clinical decision support, rare disease diagnosis, or diagnostic reasoning systems. Retrieval-augmented generation, agentic AI workflows, prompt engineering
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-tagging efforts that are being developed at McGill and TRIUMF Supervise one graduate and 1-2 summer students The appointment is initially for two years with the possibility of renewal for up to 3 years
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outside of McGill, literature reviews, involvement in bioimaging communities of practice. Attend regular lab meetings, attend relevant McGill University seminars, participate in International technology
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The Gerald Bronfman Department of Oncology (GBDO) is seeking a highly motivated Postdoctoral Researcher to contribute to leading-edge research evaluating cancer care delivery, quality of care, and patient
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postdoctoral fellowship. The successful candidate will join a national interdisciplinary research program examining rehabilitation as a critical component of Canada's health and social systems. The postdoctoral
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support, along with a competitive postdoctoral salary and benefits package. There is opportunity for travel to national and international conferences with a successfully funded project. Responsibilities
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at McGill University is recruiting a fully funded Postdoctoral Fellow to lead projects at the intersection of human genetics, proteomics, and multi-omics for drug target discovery and precision medicine in
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convection. The objective is to develop reduced but realistic models of cumulus life cycles that may be applied toward cumulus parameterization and/or machine-learning algorithms for predicting short-term