444 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" research jobs in Singapore
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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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delegated by the Principal Investigator Job Requirements PhD degree in Electronic Engineering, Computer Science, or related field Knowledge of pinching antennas, wireless communications, and machine learning
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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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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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PhD qualification degree in Electronic Engineering or Computer Science Familiarity with pinching antennas and machine learning Good written and oral communication skills Proficiency in python
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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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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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role in designing composites materials using inorganic solid electrolytes using computational modelling and machine learning. Qualifications • Ph.D. in Materials Science, Chemistry, Physics, or a
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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