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the central challenge hindering this vision: the fundamental incompatibility between text-native LLMs and the operational reality of computer networks. Directly applying LLMs is impeded by three core technical
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to quickly quantify the damage to forest plantations after a cyclone or a tropical storm. There is unrealised potential in using multi-modal computer vision methods that synthesis multi-source Earth
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enquiries to: Johan Wahlstrom (j.wahlstrom@exeter.ac.uk) Please ensure you read the entry requirements for the potential programme you are applying for. To Apply for this project please click on the following
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for the potential programme you are applying for.
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
such a promising technology, the centralised and resource-intensive nature of current LLMs conflicts with the constraints of aerial 6G networks in terms of limited computation, energy, and communication
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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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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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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read the entry requirements for the potential programme you are applying for. To Apply for this project please click the 'Apply' button
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@exeter.ac.uk ) Please ensure you read the entry requirements for the potential programme you are applying for. To Apply for this project please click the 'Apply' button Funding Comment Payment of tuition fees