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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 9 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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administrative direction of the Operations Manager and the direct supervision of the Campus Mail Coordinators, drives a mail truck and/or other delivery vehicles to distribute internal and external mail/parcels
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completion of tasks is high. The College of Nursing is a distributed college with programming and sites across Saskatchewan, which will require the incumbent to have strong communication skills (e.g. verbal
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reports to the Principal Investigator (Dr. John-Jose Nunez) and will also receive direction and some supervision from the Co-Principal Investigator Prof. Raymond Ng. They will also interact with and receive
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developing machine learning algorithms specifically designed for medical imaging applications. In addition, performs analysis of tissue images of cancer using machine learning methods that have been prototyped
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to: Prof. Shan Wang Search Committee Chair and Head, Department of Finance and Management Science Edwards School of Business, University of Saskatchewan Saskatoon, SK, S7N 5A7 E-mail: mis.search
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Into to Comm. Studies – Prof TBA (3 positions) COMS 301 Core Concepts in Critic Theory – Prof Darin Barney COMS 310 Media and Feminist Studies – Prof Carrie Rentschler COMS 360 Environmental Communication
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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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to Friday, December 19 Job Description: Prof. Guillaume Fontaine, RN, PhD is seeking two Teaching Assistants (TAs) to support the online course Fundamentals of Digital Health (NUR2 533). Key responsibilities
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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