163 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" Fellowship research jobs at Nanyang Technological University
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the brain. The key objective is to support efforts to identify how these interactions contribute to neurological disorders and to discover potential therapeutic targets. For more details, please view https
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understanding of gut microbiota’s impact on cardiovascular health, aligning with NTU’s mission to drive innovative research for societal benefit. For more details, please view https://www.ntu.edu.sg/medicine
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deployment enabling validation and demonstration of real-world applications. For more details, please view https://www.ntu.edu.sg/erian We are seeking a Research Fellow to lead the development and
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discoveries into meaningful health outcomes for patients, Singapore, and the global community. For further information, please visit: https://www.ntu.edu.sg/medicine/CMM . We are seeking a motivated Research
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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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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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, 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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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