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We invite applications for a 1-year, full-time postdoctoral fellowship (non-renewable). This is an exciting opportunity for a dynamic, impact-driven scholar committed to reimagining global readiness
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approaches (based on functional programming abstractions) to optimize the implementation of machine learning models and other digital signal processing algorithms on a specific FPGA architecture to fit within
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shaping justice access? Systemic and structural issues: How do funding models and institutional practices affect the delivery of bilingual legal services? What are the structural causes of linguistic
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geospatial modelling of agricultural management practices across Canada, particularly those related to fertilizer use and cropping practices. As part of this, the postdoctoral researcher will contribute
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metamaterial tessellations. Experimental testing of fatigue properties. Impact testing. Qualifications: Must have recently obtained a Ph.D. degree; preferably in Mechanical Engineering. Extensive experience in
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, Pekrun, Hadwin, Järvenoja, Cutumisu, Lobczowski Miller), health sciences education (Harley, Brydges, Fried, Wiseman, Sun, and Khalil), educational technology (Harley, Cutumisu, Lobczowski), advanced
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development experience using Python, ROS2 and software engineering techniques. Extensive knowledge of ML, DL and RL tools, theory and applications. Before applying, please note that to work at McGill University
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to understand the needs, experiences and challenges of people with rare musculosleketal conditions. Conduct quantitative analysis on functional outcomes for descriptive and modelling purposes. Partner with
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: Synthesize small molecules, produce samples of proteins and nucleic acids Characterization using a variety of biophysical methods including in silico modeling, ITC and UV-Vis spectroscopy Perform medicinal
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nitrogen module in the Global Change Analysis Model (GCAM). The McGill-based postdoctoral researcher will have a particular focus on synthesizing and harmonizing existing subnational data (including through