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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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and Durham University. The primary focus will be on designing and implementing deep learning and anomaly detection algorithms to analyse large-scale, real-world sensor data collected from in-service
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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
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candidate will develop machine learning and algorithmic design skills. The candidate will gain valuable multidisciplinary skills in the area of machine learning and data analytics methods and their
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optimisation model, and apply exact optimisation techniques, and metaheuristics for the optimisation of large-scale problems such as Genetic Algorithm, and/or fuzzy optimisation techniques for treating problems
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years full-time; 2-3 years part-time; MPhil: 2-3 years full-time; 3-4 years part-time; Apply now Overview Overview Research sections Formal Analysis, Theory and Algorithms (FATA) Glasgow Interactive
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image processing, from theory and algorithms to applications throughout science and technology. We particularly welcome applications from candidates with expertise and research focus on machine learning
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, path finding and routing algorithms, sense of direction, human computer interaction, cognitive navigation, intelligent mobility, and artificial intelligence. Sensor fusion and Signals of Opportunity We
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leveraging AI based protein design algorithms. Techniques used are Biomolecular NMR spectroscopy and Artificial Intelligence based protein design. References: Torres Cabán, C. C., Yang, M., Lai, C., Yang, L