463 machine-learning-"https:"-"https:"-"https:"-"https:" positions at McGill University in Canada
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position is based in the Department of Electrical and Computer Engineering. Position Summary: Under the direction of the immediate supervisor, provides administrative support for academic student affairs
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. Learning outcomes: By the end of this course, the students will be able to: Describe the purpose of primary hardware and software components of computer networks. Explain the fundamental concepts of common
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course lecturer position to teach in Winter 2026. Please include your cover letter and Curriculum Vitae and if applicable a copy of your Study/Work Permit. A brief course outline is optional. MECH 289
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course lecturer position to teach in Winter 2026. Please include your cover letter and Curriculum Vitae and if applicable a copy of your Study/Work Permit. A brief course outline is optional. MECH 289
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client service for our executive participants. The virtual training programs are an important component of the MEI learning platform and contribute to the overall MEI revenue. We are looking to extend
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. Qualifications: PhD in machine learning, with experience in applications in computer vision or medical image analysis. Strong publication record in top venues (e.g., CVPR, MIDL, MICCAI, IPMI, PAMI, TMI, MIA
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Research Assistant 1 position is based in Prof. Narges Armanfard's research lab in the department of Electrical and Computer Engineering. This Research Assistant role supports the development of AI-based
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McGill University | Winnipeg Sargent Park Daniel McIntyre Inkster SE, Manitoba | Canada | about 4 hours ago
psychological experiments including computer programming, data analysis, and eye-tracking. Before applying, please note that to work at McGill University, you must be both authorized to work in Canada and willing
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participates in their implementation and development to improve efficiency within the unit. Liaises with computer resource persons as required. Sets up and maintains databases and spreadsheets. Enters data and
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field. Demonstrated experience with single-cell and/or spatial transcriptomic analysis (Scanpy, Seurat, Squidpy, etc.). Strong programming proficiency in Python and R; experience with machine-learning