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edge AI for localized knowledge preservation; AI governance and data sovereignty in digital heritage institutions and collections; study and design of recommendation systems and ranking algorithms used
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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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Sessional Instructional Assistant - MAT302H5F - Intro to Algebraic Cryptography (emergency posting)1
University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 2 months 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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awards and award eligibility, assists in tracking and monitoring the distribution of college awards, helps maintain college award databases, performs preliminary screening of award and grant applications
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recommending changes for more efficient coordination of operations Testing and analyzing new and upgraded software, hardware, and/or business applications Preparing meeting agendas, and taking and distributing
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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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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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managing the general inbox, phone line, and responding to general inquiries in a timely manner Providing administrative support to committees including taking, transcribing, and distributing minutes
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 2 months ago
, valgrind, etc.); solid experience with performance measurements, application profiling, and performance analysis. Must have strong knowledge in Parallel Programming and Distributed Computing. Being familiar
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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