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enrolment: 40 students Estimated TA support: one 60-hour TA position for every 30 students Class schedule: Thursdays 18:00-21:00 *Please note, the delivery method for this course is currently in-person
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-efficient learning, uncertainty estimation, 3D vision. What you bring to the table Good knowledge in machine learning and computer vision Experience in programming with Python and familiarity with PyTorch
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out laboratory experiments utilizing various laboratory techniques, and determines whether additional or other tools or methods are required Provides input regarding research protocols and experiments
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to the analysis problem. Processes, organizes and summarizes data, reporting these experimental results. Monitors quality and integrity of recorded information and data. Ensures data collection and entry methods
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maintains lighting, audio, and video equipment including large venue video projectors, complex audio systems, computer systems, networking, complex theatrical lighting systems, video cameras, and video
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balances cash drawer. Maintain confidentiality of information and works in accordance with FERPA guidelines on the disclosure of information. Enter daily cash activity into computer system. Maintain parking
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and outstanding food". Responsible for ensuring the cleanliness and sanitizing of dishes, pots, pans, utensils, and equipment through manual and machine cleaning methods. Performs other related duties
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 9 hours ago
. (24S) Estimated Course Enrollment: 20 Estimated TA Support: N/A Class Schedule: Thursdays, 5:00 –7:00 Sessional Dates of Appointment: September 1, 2025 - December 31, 2025 Salary: Sessional Lecturer I
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Course description: This course focuses on the fundamental tools of calculus and its connections to engineering. The topics include methods of integration, an introduction to differential equations
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approaches specifically tailored for these low-data applications that are reliable and robust against noise. You will support our team in developing reliable machine learning methods that can be applied