290 algorithms-phd-"INSAIT---The-Institute-for-Computer-Science" positions at University of Toronto
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
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have earned a PhD degree by the time of appointment, or shortly thereafter. Alternatively, applicants must have a Master’s degree with at least five (5) years of teaching experience. Relevant fields
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in their cover letter. Tier 2 Chairs are intended for exceptional emerging scholars. Nominees should be within ten years of receiving their PhD or terminal degree in their field. Applicants who
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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 | about 3 hours ago
: MAT344H5F Intro To Combinatorics (emergency post) Course description: Basic counting principles, generating functions, permutations with restrictions. Fundamentals of graph theory with algorithms
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Sessional Instructional Assistant - MAT302H5F - Intro to Algebraic Cryptography (emergency posting)1
University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 3 hours agocryptography, from Euclid to Zero Knowledge Proofs. Topics include: block ciphers and the Advanced Encryption Standard (AES); algebraic and number-theoretic techniques and algorithms in cryptography, including
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theoretical foundations, algorithm development, and experimental validation through state-of-the-art robotics and imaging facilities. The Research Coordinator will work closely with students, postdoctoral
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, sometimes from multiple jurisdictions, to achieve sample sizes appropriate for training algorithms. This creates challenges with data security and data flows (due to legislative restrictions). Further, data
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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
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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