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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 | 3 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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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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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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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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of Biomedical Engineering, please visit https://www.bme.ubc.ca/ . HuMBL, a research lab within the SBME, specializes specifically in wearable sensor technologies and algorithm-driven health analytics to enhance
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, including algorithms, complexity, cryptography, and logic. The candidate's qualifications, experience and overall market demand will determine a candidate’s final salary offer. The salary for this position
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The University of British Columbia (UBC) | Vancouver UBC, British Columbia | Canada | about 2 months ago
be processed and displayed on a custom cloud platform. This position is part of the CLEAR project in collaboration with Rogers and the Bamfield Marine Sciences Centre and will include algorithmic
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power of data science and algorithmic research with the fields of democratic theory, political science, and public policy. Ideally, the candidate has expertise and interest in innovative research using
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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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. Key Responsibilities Lead AI/ML algorithm development for predicting plant water and nutrient uptake under varying environmental and growth conditions. Analyze multi-source data, including aerial and