176 machine-learning "https:" "https:" "https:" "https:" "The Open University" Fellowship research jobs at Nanyang Technological University
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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 Research Fellow to work closely with NTU MSE and A*STAR
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, including organizing research activities, managing projects, and contributing to grant proposals. Job Requirements: PhD degree in communication, psychology, sociology, Human-Computer Interaction (HCI
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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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Requirements: A Ph.D. degree in a related discipline (transportation engineering, computer engineering/science, or related disciplines) by December 2025. Expertise in AI, deep learning, and programming (e.g
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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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, please view https://www.ntu.edu.sg/newri . We are looking for a Research Fellow to support the research project on “Novel High Performance Piezoelectric Thin Film Composite (TFC) Nanofiltration/Reverse
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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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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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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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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