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Deadline: 31 October 2025 Details This project aims to develop new algorithms for reinforcement learning from human feedback, to effectively solve complex reinforcement learning tasks without a predefined
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electromagnetic design. We will explore advanced topologies for mmwave metasurfaces, design novel reconfiguration mechanisms, and develop intelligent algorithms to optimize scattering characteristics in real-time
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their physical optics. At the University of Sheffield, we are rewriting the rules. Our “digital lens” approach fuses novel optical designs with powerful AI and sophisticated algorithms to build microscopes
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dominant paradigm for data analytics is to run a machine learning algorithm on a fixed dataset to generate a single model. The model is then applied in a specific task for forecasting purpose. Such learning
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decision with multiple data sources. One example is to develop the semi-supervised methods and dynamic system interfacing algorithms to produce an automated and real-time information exchange across