177 machine-learning-"https:" "https:" "https:" "https:" "https:" "The University of Edinburgh" Fellowship positions at Nanyang Technological University
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progress. Ability and willingness to work some flexible hours. Extensive experience in large-scale pre-training of large language model. Experienced in developing machine learning algorithms and large
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machine learning and AI acceleration. Perform performance, power, and area (PPA) analysis of processor and accelerator designs. Publish research findings in top-tier conferences and journals and contribute
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management systems, proliferation of energy efficient solutions, creation of a “car-lite” society, digitalization of the energy system enabling a ubiquitous smart grid architecture and establishing low carbon
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Materials, Bioinspired Materials and Sustainable Materials. For more details, please view https://www.ntu.edu.sg/mse/research . We are seeking a highly motivated and interdisciplinary research fellow to
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accelerator design, verification, and physical implementation using open-source tools. Explore architecture-algorithm co-design for machine learning and AI acceleration. Perform performance, power, and area
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independently and as part of a team Experience with machine learning and AI applications in engineering is advantageous We regret to inform that only shortlisted candidates will be notified. Hiring Institution
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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 pursuing
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of scalable machine learning systems using convex/nonconvex optimization and federated learning methods. Develop software prototypes for AI-for-Science systems tailored to scientific discovery and data
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, please view https://www.ntu.edu.sg/spms We are looking for a Research Fellow to complete the project on low dimensional quantum materials and devices and ensure the success of the project and support other
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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