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behaviour in a practical, real-time monitoring system requires advances in both sensor engineering and behavioural data interpretation. This PhD project aims to develop a next generation environmental
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benefits toward a net-zero future. Project specific entry requirements: The PhD should have background in Materials, Chemistry, Engineering etc. Potential PhD programme of study: PhD in Renewable Energy
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the framework in an industrial testbed and evaluate accuracy and computational performance under realistic load conditions. The student will work closely with Chameleon Technology, the UK market leader in energy
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good Master’s degree in Mathematics, Statistics, Computer Science, Engineering, Physics, with good programming, analytical problem-solving, writing and presentation skills. Please direct project specific
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programme you are applying for. To Apply for this project please click on the ‘Apply’ button above.
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settings, while providing essential process insights (such as electrode spacing, solvent chemistry, and operational voltage/current conditions) to support further technological development. Project specific
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to commercialise the outputs of the project. Project specific entry requirements: Minimum 2.1 (or equivalent) degree in Zoology/Biology, Engineering or Computer Science/Data Science. Department: Ecology and
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
supervisor Prof. Shiqiang Wang (https://shiqiang.wang ) has recently moved to the University of Exeter after nearly 10 years of industry experience in the United States. He will bring a unique vision, hands-on
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strategic priorities in Digital Twins, Environmental Intelligence, and Data-Driven Engineering, using advanced computational modelling to support ecosystem resilience and sustainable management. The project’s
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Sciences at the University of Exeter’s Cornwall campus, home of the internationally recognised Environment and Sustainability Institute. Local government and civic society groups in Cornwall will offer a