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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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. The potential of lithium extraction from brine and seawater, integration with renewable energy sources make this technology promising for low-cost and decarbonized lithium production. However, ceramic membranes
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are fundamentally limited by a "one model for one task" design philosophy. This approach incurs prohibitive engineering costs and yields brittle solutions with poor generalisation to new network conditions, trapping
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
-latency, and scalable operation in aerial 6G networks. In this regard, Large Language Models (LLMs) have recently emerged as a key technology to achieve adaptive 6G spectrum management. The core idea of LLM
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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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the two primary supervisors’ expertise in behavioural field biology with engineering and data science approaches to develop and test tools for behavioural research and ecological monitoring. The Cornish
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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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The University of Exeter’s Department of Engineering is inviting applications for a PhD studentship co-funded by the partner Hydro International and University of Exeter Faculty of Environment
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thinking and design, under the supervision of Tim Hughes (a researcher in Systems Engineering and its interdisciplinary applications, and a lecturer in Applied Data Science), with co-supervision from