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with fabrication engineers to translate physical processes into machine learning models, design and train deep learning architectures, and evaluate their ability to generalise across different process
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, spectroscopy, and electrical performance measurements. You will work closely with fabrication engineers to translate physical processes into machine learning models, design and train deep learning architectures
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the mentorship of leading experts in one of the following priority research areas: Research area 1: Intelligent Structural Optimization using Physics-Informed Reinforcement Learning Research area 2: AI-Enhanced
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-Informed Reinforcement Learning Research area 2: AI-Enhanced Digital Twin Framework for Smart and Sustainable Advanced Manufacturing Research area 3: Advanced Multifunctional Materials The ideal candidates
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for light–matter interaction in hyperuniform disordered plasmonic structures, including electromagnetic modelling, optimisation of metal–dielectric–metal resonators, and physics-informed machine-learning
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within a Research Infrastructure? No Offer Description The University of Southampton (UoS), in partnership with Ingenium Biometric Laboratories (IBL), is seeking a Research Fellow to design and implement a
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application of innovative Machine Learning (ML) frameworks to understand and predict the global hydrological cycle. The role will require bridging the gap between process-based physical modeling and scalable