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activities focus on active learning, personal reflection, Indigenous knowledge, and building hope and resilience through collaborative action. More information about the course is here: https://www.mcgill.ca
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Atmospheric and Oceanic Sciences, Biology, Chemistry, Earth and Planetary Sciences, Geography, Mathematics and Statistics, Physics, Psychology, and the School of Computer Science, as well as interfaculty and
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systems. Relevant disciplinary backgrounds may include a blend of veterinary medicine, animal science, genomics, microbiology, environmental science, agricultural engineering, and computational biology
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description, please visit: https://www.mcgill.ca/engineering/about-us/employment-opportunities How to apply: ***In accordance with Article 12.02 of the AGSEM Teaching Assistant collective agreement: All
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information, please visit https://wcvm.usask.ca. Extensive opportunities for collaborative research exist within WCVM and the University (Colleges of Medicine, Pharmacy and Nutrition, Dentistry, Engineering
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educational technology and tools Have demonstrated skills and abilities in the delivery of flexible learning materials, the application of instructional technology and in the application of adult learning
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you want to make a meaningful impact in life sciences research by developing cutting-edge technology for data sharing? Join the Canadian Centre for Computational Genomics (C3G) at McGill University
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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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well as of presentations at scientific conferences. Qualifications: Education – Master in relevant field (physics, engineering physics, material science) A background in one or more of the following areas: experimental and
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educational technology and tools Have demonstrated skills and abilities in the delivery of flexible learning materials, the application of instructional technology and in the application of adult learning