97 machine-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at McGill University in canada in Canada
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: Department of Electrical and Computer Engineering Course Title: Electric Circuits 2 Course Code: ECSE 210 Estimated Number of Positions: 1 Total Hours of Work per Term: 102 Hiring unit: Department of
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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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: Department of Electrical & Computer Engineering Term: Fall 2026 Course subject code: ECSE 316 Course Title: Signals and Networks Course Credits: 3 credits Location: ARTS W-120 Schedule: Monday and Wednesday
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are coordinate measuring machines and measurement probes. Basic understanding of geometric and dimensional tolerances. Past TA duties must include marking or demonstrating or tutoring. Proficiency in English
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, data, and systems meet – particularly around AI and data science. As part of its mission, CDSI offers workshops on topics related to programming languages, data science, machine learning, natural
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minimal direction. Computer literate with all Microsoft Office tools. Experience with Banner (SIS) and Minerva is an asset. Excellent communication skills, written and verbal. Experience leading discussion
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to bylaws, rules, and regulations, including ensuring no machines are used or brought into this area. Foster attention to cleanliness, health, and safety by all users of the facilities and ensure
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practical knowledge of machine learning with large datasets Experience : Experience with cloud-based, parallel, or distributed computing environments Hands-on experience developing or deploying production ML
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practical knowledge of machine learning with large datasets Experience : Experience with cloud-based, parallel, or distributed computing environments Hands-on experience developing or deploying production ML
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, topographic data, land-cover data, and other environmental predictors. Designs and evaluates statistical and machine-learning approaches for modelling plant species distributions and assessing environmental