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, scalability and Amdahl's law, Flynn taxonomy, vector processing and parallel computing architectures. Reference: https://artsci.calendar.utoronto.ca/course/csc367h1 Estimated course enrolment: 125 students per
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and experience working collaboratively with clinicians on program design, research projects and research implementation. The successful candidate will ideally have experience and strong interest in
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 1 hour ago
: Course Number and Title: STA380H5S LEC101 Computational Statistics Course Description: Computational methods play a central role in modern statistics and machine learning. This course aims to give an
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use of digital technology in facilitating healthcare access and experience working collaboratively with clinicians on program design, research projects and research implementation. The successful
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 1 hour ago
. Building and maintaining Quercus course shell. Design course syllabus, content and assignments in close consultation with Program Director. Liaise with Program Director regarding course content and
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multidisciplinary care in the neuro-oncology program. The candidate should display interest and training in open and endoscopic skull base approaches and collaborative multidisciplinary neuro-oncology care
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3 application form located here: https://uoft.me/CUPE-3902-Unit-3-Application-Form to: Tanya Pitel, CIRHR Program Assistant Centre for Industrial Relations and Human Resources University of Toronto
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2, 2026, 11:59pm EST Course number and title: MMG3001Y: Advanced Human Genetics Course description: This two-term graduate course is offered within the MHSc Medical Genomics program, and is restricted
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flexibility. The successful candidate will join a dynamic nine-member division of orthopedic surgery and a multidisciplinary trauma program. It is expected that the chosen candidate will contribute
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uncertainty and learning from new data. The course introduces the basics of Bayesian inference and Markov chain Monte Carlo methods, then shows students how to compute and make inferences for complex data