187 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" 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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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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-scale modelling, spectroscopic analysis, and more. The group has extensive research experience in theoretical calculations, energy science, and machine learning. The research emphasizes the integration
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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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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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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