291 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" Fellowship research jobs in Singapore
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a valuable opportunity to collaborate with and learn from other scholars and experts within the CIL Oceans Law and Policy Team, in a dynamic and supportive research environment. CIL supports both
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A Postdoctoral Fellow position is available in Prof. Tong Ling’s Lab (https://www.tonglinglab.com ) at Nanyang Technological University, Singapore. The successful candidate will work with our
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Research Fellow / Associate Research Fellow / Senior Analyst / Research Analyst (Military Studies Programme) The S. Rajaratnam School of International Studies (RSIS), a Graduate School of Nanyang
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School graduates over a thousand students who are ready to take on great ambitions and challenges. For more details, please view: https://www.ntu.edu.sg/eee We are seeking a Senior Research Fellow (SRF
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superlattices (twistronics). The role will focus on developing and applying theoretical models and computational quantum chemistry and machine learning methods to uncover novel properties and phenomena in low
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . Key Responsibilities: The Research Fellow will be in charge of modelling, simulation, and analysis for projects
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staff position within a Research Infrastructure? No Offer Description As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the
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third largest university by intake in Singapore. SIT’s mission is to innovate with industry, through an integrated applied learning and research approach, so as to contribute to the economy and society
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School graduates over a thousand students who are ready to take on great ambitions and challenges. For more details, please view: https://www.ntu.edu.sg/eee We are looking for a research fellow to carry
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international collaborators across clinical, academic, and industry settings to develop privacy-preserving machine learning approaches, federated learning frameworks, and interpretable algorithms for multimodal