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Date Posted: 05/30/2025 Req ID: 37931 Faculty/Division: Faculty of Music Department: Faculty of Music Campus: St. George (Downtown Toronto) Description: JDM3619H Digital Media Distribution A
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Sessional Lecturer - AMS402H1F: Interfacing Cultures: AI, Platforms, and Algorithmic Politics Across
Study of the United States is seeking to hire one Sessional Lecturer for the following course: Course number and title: AMS402H1F: Interfacing Cultures: AI, Platforms, and Algorithmic Politics Across
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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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, Python, or C/C++, with the ability to develop custom scripts and algorithms for data analysis and modeling. Familiarity with rheological characterization techniques, such as rheometry or viscometry
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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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), debuggers, code verifiers and unit test frameworks and gain experience in graphical user interface design and algorithm development. Posting end date: July 11, 2025 Number of positions (est): One (1) position
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, strings, pointer-based data structures and searching and sorting algorithms. The laboratories reinforce the lecture topics and develops essential programming skills. Estimated course enrolment: ~150
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description: Geologic hazards: earthquakes, volcanoes, landslides, tsunamis. The distribution and politics of natural resources, including petroleum and ore deposits. Nuclear power and nuclear waste disposal
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- Mathematical Statistics I (0.5FCE) This course is designed for graduate students in Statistics and Biostatistics. Review of probability theory, distribution theory for normal samples, convergence of random
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 2 days ago
statistics, contingency tables, normal probability distribution, and sampling distributions. The second half of the course introduces inferential statistical methods. These topics include significance test for