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: Department of Electrical & Computer Engineering Term: Winter 2026 Course subject code: ECSE 552 Course Title: Deep Learning Course Credits: 4 credits Location: ENGMD 279 Schedule: Monday and Wednesday: 8:35 am
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initiatives, or with programming language experience and experience in machine learning and health informatics. An Understanding of the digital health space, is expected but not essential. Good Communication
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learning, offers powerful solutions to automate these tasks and provide reliable real-time information. This doctoral project is part of a 5-year research chair on Computer vision applied to the swine sector
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University of Montreal-CHU Sainte Justine Children's Hospital Research Center | Canada | 28 days ago
Project OverviewSpeech and language disorders affect millions of children worldwide, limiting communication and quality of life. This PhD project will explore the use of Reinforcement Learning (RL
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, harvest and store clinical data and methods used to create predictive models (including but not limited to methods associated with machine learning). Furthermore, issues related to delivery of predictive
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 1 day ago
: Department of Computer and Mathematical Sciences Campus: University of Toronto Scarborough (UTSC) Description: The Department of Computer and Mathematical Sciences at the University of Toronto Scarborough
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engineering, electrical engineering, data science, or a related field. Skills in embedded systems development, electronics, or IoT (C/C++, Python, Arduino/ESP32, etc.) OR in machine learning and sensor data
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to thoughtfully and conscientiously shape the fi eld of the future. With our robust and comprehensive educational training programs (DDS, MSc, PhD degrees, IDAPP and other specialty programs), our emphasis
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Koziarski Lab - The Hospital for Sick Children | Central Toronto Roselawn, Ontario | Canada | about 1 month ago
program at The Hospital for Sick Children, University of Toronto, and the Vector Institute. Our research group focuses on developing machine learning-based pipelines that leverage generative models and
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behavioural issues and how they affect the design and usability considerations related to clinical applications and devices. Students will learn about how artificial intelligence can be used to support