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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . We are looking for a Research fellow to work on the development of Physics-informed neural networks (PINNs
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, including deep neural networks and physics-informed neural networks, to analyse large datasets from gyrokinetic and fluid simulations of plasma turbulence Develop and train reduced-order models that capture
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spatiotemporal systems by combining physics-driven baselines with data-driven correctors. Formulate and solve inverse problems using Physics-Informed Neural Networks and relevant methodologies. Conduct rigorous
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, such as, geometric/topological/algebraic data analysis, geometric/topological deep learning, Math for AI, categorical deep learning, sheaf neural networks, PINN/KAN models, neural operators, etc, and
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, leveraging renewable energy inputs. By utilizing deep neural networks (DNN), a surrogate model for the i3C process is developed, facilitating rapid evaluation and optimization. Additionally, a data-driven