231 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" research jobs at Nanyang Technological University
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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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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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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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in Singapore since 2021, the implementation of Full-Subject-Based Banding in all schools, and the widespread use of machine learning technologies have led to seismic changes in the educational system
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in empirical analysis using econometric, machine-learning, and language-modeling techniques. Conducting literature reviews and synthesizing existing academic research to support ongoing projects
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aims to improve electrodialysis (ED) for REE separation by developing advanced membranes and integrating AI-driven optimization techniques. By combining materials innovation with machine learning
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scientific leaders and researchers. Job responsibilities The project aims to advance the use of machine learning techniques to model and understand plasma turbulence in magnetically confined fusion plasmas