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: Machine learning/deep learning model development for biomolecular data analyses and prediction Research Area: Data science and computational chemistry Required Skills: A Ph.D. in relevant field within
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: Course title: APS360H1 Y – Applied Fundamentals of Deep Learning Course description: A basic introduction to the history, technology, programming and applications of the fast evolving field of deep
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: Desautels Course Title and Course Number: Introduction to Artificial Intelligence and Deep Learning MGSC 673 Estimated Number of Positions: 1 Total Hours of Work per Term: 45 Position Summary: Course work
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of Computer Science Position summary: Develop and enhance the results on an urban transit scheduling application that combines deep learning and reinforcement learning to optimize transit networks Design and run a
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academic technology projects to improve an organization's overall programs and services. SKILLS: Deep understanding of research-informed theories of learning, curriculum design/development, innovative
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and deep learning. Strong Computer Vision background with publications in top/mid-tier conferences e.g., CVPR, ICCV, ECCV, WACV. Strong mathematical understanding and skills in applying Machine Learning
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aspects of the human condition.” Over time, this emphasis on reflective learning has cultivated a deep interest in social justice, expressed through unique interdisciplinary programs (e.g., Critical
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at the University of Toronto is Canada’s premier medical faculty, ranked 3rd in the world in 2024 for clinical medicine. With a deep history of global leadership in ground-breaking research and innovation
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the collection, processing, and analysis of physiological data from recreational and elite athletes across various exercise protocols. Utilizing machine learning and deep learning algorithms, integrate multi-modal
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recovery. Oversee the collection, processing, and analysis of physiological data from recreational and elite athletes across various exercise protocols. Utilizing machine learning and deep learning