443 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" research jobs in Singapore
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storage materials, and employing machine learning and high throughput for the discovery of new electrode materials and electrolyte systems. 1. Holds a PhD degree in chemical engineering, chemistry
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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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, Singapore, and the broader public. For more details, please view https://www.ntu.edu.sg/medicine/CMM . The role will involve investigating the influence of modifiable environmental risk factors, dietary and
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function. Over the years, SBS has attracted talented individuals from around the world and Singapore to join as scientific leaders and researchers. For more details, please view https://www.ntu.edu.sg/sbs
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international law. More on CIL can be found here: https://cil.nus.edu.sg/ About the Oceans Law and Policy Team: Ocean Law & Policy is one of CIL’s core programme areas and was established over 10 years ago. It
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insights from cutting-edge clinical and translational research. More information on the Department’s research portfolio may be obtained from https://medicine.nus.edu.sg/obgyn/. The Core Support Faculty will
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computer vision and machine learning. To produce research reports and/or publications as required by the funding body or for dissemination to the wider academic community. To provide guidance and support to
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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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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