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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 1 day ago
more advanced concepts such as tools for supervised/ unsupervised learning that will be helpful for deep learning focused courses. Estimated course enrolment: 35 Estimated TA support: 1 Class schedule
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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates
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approaches to remove atmospheric particulate (e.g., PM2.5) pollution. The math-based subgroup focuses on the use of deep learning and generative AI to address critical problems for the electric grid and broad
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, applying deep domain knowledge and advanced quantitative methods to inform critical development decisions. At Northeastern University, the Fellows will engage in scientific publication, conference
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integrates neuroimaging, sleep measurement, digital phenotyping, electronic health record (EHR) data, and deep clinical phenotyping to identify predictors of symptom trajectories and functional outcomes in
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the dynamics of potentially illegal waste deposits. The research will apply deep learning and computer vision techniques to identify regions within an image where there is an increase or decrease in
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status, sexual orientation, gender identity, or gender expression. To learn more about diversity at the U: http://diversity.umn.edu Employment Requirements Any offer of employment is contingent upon
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, presentation and communication skills Applicants must demonstrate a genuine interest in/motivation towards the area of deep learning and computational linguistics. Desirable Experience in NLP / Computational
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that nurtures the whole person-mind, body, and spirit. Here, you will find a deep sense of belonging, a culture that prioritizes well-being, and the opportunity to grow your career while being a force for good in
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of the following areas: Wireless and satellite communications AI/ML for dynamic networks including Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models