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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 | 2 days 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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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 3 hours ago
cryptography, 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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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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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 3 hours ago
% vacation pay Minimum qualifications: Familiarity with scalable technologies such as Apache Spark, Amazon Lambda, Cassandra, MongoDB, Redis, CUDA, Parallel Algorithms, Docker, Kubernetes, Squid. Familiarity
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radio medium, radio communication system examples. Link budget: cable losses, propagation loss, antenna gains. Basic concepts of propagation: path loss, multi-path propagation and fading. Raleigh and
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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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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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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
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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