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, addressed to the Associate Undergraduate Chair, Prof. Heidi Bohaker and must include: Closing Date: June 16, 2026
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to history.cupe3@utoronto.ca , addressed to the Associate Undergraduate Chair, Prof. Heidi Bohaker and must include: • application form available at https://uoft.me/CUPE-3902-Unit-3-Application-Form • Updated
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to history.cupe3@utoronto.ca , addressed to the Associate Undergraduate Chair, Prof. Heidi Bohaker and must include: • application form available at https://uoft.me/CUPE-3902-Unit-3-Application-Form • Updated
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to history.cupe3@utoronto.ca , addressed to the Associate Undergraduate Chair, Prof. Heidi Bohaker and must include: • application form available at https://uoft.me/CUPE-3902-Unit-3-Application-Form • Updated
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Undergraduate Chair, Prof. Heidi Bohaker and must include: • application form available at https://uoft.me/CUPE-3902-Unit-3-Application-Form • Updated CV • teaching evaluations • two letters
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 27 days ago
, valgrind, etc.); solid experience with performance measurements, application profiling, and performance analysis. Must have strong knowledge in Parallel Programming and Distributed Computing. Being familiar
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: Course number and title: MIE1624F/S – Introduction to Data Science and Analytics Course description: The objective of the course is to learn analytical models and overview quantitative algorithms
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 7 days ago
Sessional Lecturer- BIO412H5 F: Climate Change Biology Course description:. Climate change is affecting life on earth at all levels from cells to ecosystems. As a result, shifts in the distribution
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technical subjects such as programming, data science, machine learning, and algorithmic fairness is highly desirable. Candidates must have teaching experience in a degree-granting program, including lecture
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machine learning algorithms. It also serves as a foundation for more advanced ML courses. The students will learn about ML problems (supervised, unsupervised, and reinforcement learning), models (linear and