119 machine-learning-"https:"-"https:"-"https:"-"https:"-"U.S"-"U.S" Fellowship positions in Singapore
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and
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and Mathematical Sciences | NTU Singapore We are looking for a Research Fellow to study quantum materials via Machine Learning. The role will focus on develop Machine Learning technique to help DFT
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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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machine learning algorithms. • Has laboratory experience in designing, conducting, and instrumenting structures. • Strong written and spoken communications. • Open to fixed-term contract Apply now
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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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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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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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that delivers real-time, hyperlocal information on urban heat risks in tropical cities. Leveraging Doppler lidar–based microclimate studies and machine learning, the research emphasizes vulnerable groups
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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