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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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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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), machine learning (ML), deep learning (DL) and Data science methods for medical image analysis, to autonomously grade the fundus images from large datasets. This will be supported by Professor Neil Vaughan
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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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programme you are applying for. To Apply for this project please click on the following link - https://www.exeter.ac.uk/study/funding/award/?id=5733
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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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programme you are applying for. To Apply for this project please click on the ‘Apply’ button above.
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: Advancements in biosensor technology are at the forefront of modern biomedical research, addressing the growing need for precise, real-time monitoring of biomolecules and overcoming critical challenges in
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the Southwest. Geospatial and engineering analyses will identify optimal sites and system configurations, while collaboration with the Law School will assess legal and regulatory frameworks, planning constraints
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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