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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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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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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
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | about 4 hours ago
, including those which help mitigate climate change such as offshore wind farms. Applying and advancing techniques such as Deep Learning (ANN), Reinforcement Learning (RL), and Deep Reinforcement Learning (ANN
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At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our
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, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment
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to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness, brings rich diversity
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The University of British Columbia (UBC) | Vancouver UBC, British Columbia | Canada | about 2 months ago
Infrastructure? No Offer Description At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty
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workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness