642 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at Nanyang Technological University
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of being An Institute of Distinction: Leading the Future of Education and our mission to Inspire Learning, Transform Teaching and Advance Research. Read more about NIE here . The National Institute
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science. Familiarity with ship data processing, ship performance analysis, machine learning algorithms (Deep learning, Reinforcement learning, etc.). Proficiency in written and spoken English - essential for data
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Probability/Statistics with an interest in Statistical learning problems -- especially from a theoretical perspective See also other requirements and info listed here: https://www.ntu.edu.sg/spms/about-us
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vision by designing, developing, and optimizing robust distributed learning frameworks and deep learning models for visual recognition and person re-identification. Key Responsibilities: Design and conduct
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innovation. The Platforms Engineering Group builds and operates the infrastructure and systems that enable AI practitioners across AISG's programmes to develop, train, and deploy machine learning models
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deployment enabling validation and demonstration of real-world applications. For more details, please view https://www.ntu.edu.sg/erian You will be part of a dynamic research team working on topics relevant
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fields. You will be an integral member of an inter-disciplinary Science of Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical
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Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical learning difficulties in kindergarten and early primary level students
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Science of Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical learning difficulties in kindergarten and early primary level students
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Science of Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical learning difficulties in kindergarten and early primary level students