940 electric-machine-design-"https:"-"https:"-"https:"-"https:"-"https:" positions at McGill University in canada
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; • Must be a current McGill graduate student; • Must be punctual. Hourly Salary: (AGSEM Invigilator) $18.00 Deadline to Apply: 2026-09-20 McGill University hires on the basis of merit and is strongly
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awareness of current social media trends; familiarity with social scheduling tools an asset. Must be a current McGill student able to work independently on a flexible schedule. Before applying, please note
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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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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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Canada and the US in accordance with established guidelines and procedures. Remain attentive to current enrolment capacity for programs in high demand and enrolment objectives for programs experiencing
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outstanding potential who are doctoral students and who plan to have completed the requirements for their Ph.D. by December 2027. Preference will be given to candidates whose research aligns with Faculty
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will work for five hours per week, for a total of 60 hours. They will be paid an hourly rate of $18.60/hour, a rate compatible with current rates negotiated between the university and AMURE, the union
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working in a workshop/machining environment Interest in health and safety A graduate university student with experience in a design team or relevant engineering training is considered an asset. Different
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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