151 computational-physics-"https:"-"https:"-"https:"-"https:"-"Ulster-University" Fellowship positions 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 Postdoctoral Fellow to contribute to building computational
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-dimensional material systems, contributing to NTU’s leadership in physics, materials science and engineering research. Key Responsibilities: Conduct independent and collaborative theoretical and computational
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learning algorithms to support research in IDMxS. Key Responsibilites: Apply/ improve/ develop machine learning algorithms to process (e.g., classify, predict) data/ images collected by IDMxS. Help supervise
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proposal preparation Requirements: PhD degree in chemistry, physics, chemical engineering, computer science, or other related fields Research background in theoretical catalysis/materials science, AI/ML
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of nonlinear optics through the integration of artificial intelligence. The successful candidate will lead projects that combine the development of Physics-Informed Neural Networks (PINNs) with advanced fiber
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on managerial decision-making and strategic processes in AI-enabled organizational contexts. Process and analyze multi-source survey, experimental, and organizational datasets, and support preparation of research
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initiatives. Oversee day‑to‑day laboratory operations, facilities, and a team of researchers, while driving cost efficiency and process improvements. Conduct hands‑on experimental work and support research
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SPMS is a School under NTU College of Science. Our School is organized into two divisions: the Division of Mathematical Sciences and the Division of Physics and Applied Physics. We are home to
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to the applications of mathematics in cryptography, computing, business, and finance. PAP covers many areas of fundamental and applied physics, including quantum information, condensed matter physics, biophysics, and
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in numerical analysis, partial differential equations (PDEs), and scientific computing. Solid background in machine learning theories, with specific experience in Physics-Informed Machine Learning