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Date Posted: 07/31/2025 Req ID: 44594 Faculty/Division: Faculty of Arts & Science Department: Department of Computer Science Campus: St. George (Downtown Toronto) Description: Course number and
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lives, increase profits and minimize resource usage. Considerable attention in the course is devoted to applications of computational and modeling algorithms to finance, risk management, marketing, health
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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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with our interdisciplinary team of scientists and engineers. Responsibilities include but are not limited to Experimental Design and Execution, Computational Modeling, Prototype Development, Data
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. In order to address systemic barriers and increase diversity in the Canada Research Chairs Program and meet government-mandated requirements , selection will be limited to candidates who identify as
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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 | 1 day 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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), 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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promising candidates with computational tools and machine learning algorithms, and elucidating structure-property relationships of emerging molecules, polymers, solid-state materials, formulations, etc. Tasks
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characterize large quantities of candidate molecules, calibrating theoretical models with experimental data, predicting promising candidates with computational tools and machine learning algorithms, and
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characterize large quantities of candidate molecules, calibrating theoretical models with experimental data, predicting promising candidates with computational tools and machine learning algorithms, and