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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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Digital Twin Framework for Smart and Sustainable Advanced Manufacturing Research area 3: Advanced Multifunctional Materials The ideal candidates would have a background in machine learning, manufacturing
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would have a background in machine learning, manufacturing, characterization, and testing of novel high performance multifunctional materials. During the project, you will conduct independent research
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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
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or machine learning. Excellent programming skills in Python and deep learning frameworks A collaborative mindset and interest in socially impactful research. Experience with sign language data, multimodal
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component disciplines; in explainable multi-modal deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in
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deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in human factors with applications