179 computer-programmer-"St"-"FEMTO-ST"-"St" Fellowship positions at Nanyang Technological University in Singapore
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of deliverables. Lead the design, fabrication, and characterization of conductive polymer fibers and their integration into wearable sensing and thermoregulation devices. Programme experimental protocols, data
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The Climate Transformation Programme (CTP) aims to develop, inspire and accelerate knowledge-based solutions and educate future leaders to establish the stable climate and environment necessary
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research, quantum technologies, artificial intelligence, advanced communications and cybersecurity capabilities. The work will be in joint collaboration with the NRF CREATE programme Singapore Aquaculture
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project: “Climate Transformation Program (CTP): Cross Cutting Theme 1 – Sustainable Societies” funded by the MOE Tier 3C Grant. CTP aims to develop, inspire, and accelerate knowledge-based solutions and
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Medical School. In August 2024, we welcomed our first intake of the NTU MBBS programme, that has been recently enhanced to include themes like precision medicine and Artificial Intelligence (AI) in
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be required to lead a project under a larger research programme between Singapore and China. The candidate should fulfill the following responsibilities: Design, perform, and optimize experiments with
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Nanyang Technological University’s National Centre for Research in Digital Trust (DTC) is a Trust Technology Research Centre to execute a national program to help put Singapore into a strong trust
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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research that covers the energy value chain from generation to innovative end-use solutions, motivated by industrialisation and deployment. ERI@N has multiple Interdisciplinary Research Programmes which
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems