197 parallel-and-distributed-computing-"DIFFER" Fellowship positions at Nanyang Technological University
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; (3) to perform the developed model for different climate change and emission scenarios; and (4) to analyze the model results of various air pollutants and their health burdens under climate change
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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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Responsibilities: To carry out a comprehensive literature and market survey on robotic communication protocol design. To develop a new framework that allows different robots to communicate with each other and, most
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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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The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational
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compatibility analysis of materials for different components Mechanical characteristics evaluation and optimization Support mechanical and chemistry relevant qualification and failures analysis Drive specific
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conducting research in the fields of electrocatalysis and electrochemical analysis. Key Responsibilities: Electrode surface modifications using different chemical compounds and compositions. Electrochemical
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reinforcement learning using degraded human feedbacks. Develop distributed localization approaches for multi-agent systems. Investigate the suitability of various sensors for robot navigation in human-sense
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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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, 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