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RAMQ and MED-ECHO linked datasets, to generate high-quality evidence that informs cancer care delivery and health policy. Develop innovative research questions, analytic strategies, and methodological
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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
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: Steinberg Centre for Simulation and Interactive Learning (SCSIL) The Surgical Performance Enhancement and Robotics (SuPER) Centre in collaboration with Steinberg Centre for Simulation and Interactive
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manipulation and spectroscopy. Problem-solving abilities, attention to detail, and the capacity to adapt to an evolving work environment. Demonstrated ability to learn new tools and applications quickly and
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science, machine learning or a related field. Strong experience working with clinical, biomedical, genetic, electronic health record, or health administrative data. Experience with large language models
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: Develop advanced bioimaging methods and apply them to study the molecular mechanisms that paxillin and its binding partners use to regulate cell migration. Will learn cell biology, advanced microscopy
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monitoring. Familiarity with computational image analysis, scripting (Python, MATLAB), or machine learning–based image workflows. Experience with method development, imaging assay optimization, or pipeline