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National University of Singapore (NUS). SCELSE brings together expertise from the life sciences, engineering, and natural sciences to advance the understanding, engineering, and control of complex microbial
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laser experimentation to study and control complex nonlinear dynamics. Key Responsibilities: Develop and implement Physics-Informed Neural Network (PINN) models to simulate, predict, and analyze complex
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transfer, fluid–solid interactions, and pressure drop in complex thermal structures. Design and train physics-guided surrogate models (e.g. neural networks with embedded physical constraints) for rapid
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