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The Institute for Digital Molecular Analytics and Science (IDMxS) of the Nanyang Technological University, Singapore, is searching for a Research Fellow (RF) to develop artificial intelligence
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of Materials Science and Engineering at National University of Singapore (NUS). With a strong focus on industrial projects, we are developing our exciting technologies beyond the lab scale. As a Research Fellow
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. To collaborate with interdisciplinary teams, including experts in physics, chemistry, and those who work on quantum computing hardware. To perform any other duties related to the research program. Job Requirements
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Materials, Bioinspired Materials and Sustainable Materials. For more details, please view https://www.ntu.edu.sg/mse/research . We are looking for a battery scientist to develop battery management system. Key
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Fellowship Programme) and with external partners. Job Requirements: Candidates must have a PhD in one of the relevant fields of Asian Studies, international political economy, political science, public policy
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postdoctoral research position in the Department of Physics at the National University of Singapore, in the research group of Assistant Professor Marc Hon. The successful candidate will develop and apply modern
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that are relevant to industry demands while working on research projects in SIT. The successful candidate will contribute to an innovation-driven project aimed at developing a Next-Generation Sequencing (NGS) library
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Responsibilities: Conduct in-depth research in sublinear time and learning augmented algorithms. Design, develop, and implement novel algorithms and models. Publish research findings in leading international
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reference. Collaborating with AI scientists to provide domain insights that aid in the development of AI models. Performing experimental verifications of materials recommended by AI models. Publishing
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hardware technologies. Conduct hardware performance evaluations (e.g. area, power, efficiency). Develop software strategies for communication-aware task scheduling and runtime optimization. Explore low-power