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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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), 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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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 10 days ago
(Nonessential): Experience with maintaining websites and distributing information on digital platforms. To be successful in this role you will be: Communicator Diplomatic Meticulous Multi-tasker Organized
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distribution of space and room access Reconciling accounts and receiving invoices and approving payment for internal trades and external contractors Essential Qualifications: Advanced College Diploma (3 years
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estimation theory, sampling distributions, hypotheses testing, multiple regression analysis. Students will learn the tools used in economics and finance to model and address randomness and uncertainty
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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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concepts of fairness, equality, and equity) in law and human rights, including substantive, procedural, and distributive aspects. Course Learning Objectives: By the end of the course, students will be able
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readings, reading guides as applicable, narrative assignments and rubrics Coordinating distribution and scheduling of assignments, marks and feedback Preparing and delivering weekly narrative and leadership
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credits Distribution Requirements: Humanities Estimated course enrolment: 200 Estimated TA support: 250 hours Class schedule: Tuesdays, 1:00 pm — 3:00 pm *Please note, this course is scheduled to be
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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