1,439 parallel-computing-numerical-methods-"Simons-Foundation" positions at McGill University
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: October 01, 2025 Applications for this course will be accepted until OCTOBER 15, 2025 Course Title COMP 208 - 001 Computer Programming for Physical Sciences and Engineering Term Winter 2026 Credit 3
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Introduction to Computational Materials Design Overview: Introduction to modelling and simulation techniques in materials engineering: quantum mechanics and atomistic simulation (i.e. Monte-Carlo and Molecular
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experience Hiring Unit: School of Computer Science Course Title: Reinforcement Learning Subject Code: COMP 579 Location: Downtown Campus Schedule: Deadline to Apply: 2025-10-16 McGill University hires
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the degree requirement into equivalent education and experience Hiring Unit: School of Computer Science Course Title: Introduction to C++ Subject Code: COMP 322 Location: Downtown Campus Schedule: Friday
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Computer Science Course Title: From Natural Language to Data Science Subject Code: COMP 345 Location: Downtown Campus Schedule: Deadline to Apply: 2025-10-16 McGill University hires on the basis of merit and is
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). As is the standard across Canada, we also run a busy Neonatal Follow-up Program, with over 800 outpatient visits per year, during which the health and development of former premature infants is closely
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currently do not hold a TAship or do not have a profile in Workday: please apply externally using an external email address (do not use your @mail.mcgill.ca). Hiring Unit: School of Computer Science Course
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currently do not hold a TAship or do not have a profile in Workday: please apply externally using an external email address (do not use your @mail.mcgill.ca). Hiring Unit: School of Computer Science Course
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project, funded by Health Canada since 2004 under the Official Languages Health Program, is focused on initiatives to build and maintain the capacity of bilingual health and social services professionals in
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Lecturer (BIOS 691) Winter 2026 BIOS 691 Special Topics in Biostatistics (4 credits) Winter 2026 BIOS 691: The aim of this course is to provide researchers with an introduction to practical Bayesian methods