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, GNSS positioning is highly susceptible to errors from atmospheric distortions, multipath effects, and receiver noise. Recent advances in deep learning have shown that data-driven pseudorange correction
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storm to use these technologies and/or visit the affected area to evaluate storm-related tree damage. Therefore, to support sales planning and the safety of foresters working in the field, there is a need
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existing models of reliability, performance, and safety. Similarly, in dynamic crowd scenarios, assumptions about orderly movement can break down due to panic or unexpected human behaviour, leading
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that challenges existing models of reliability, performance, and safety. Similarly, in dynamic crowd scenarios, assumptions about orderly movement can break down due to panic or unexpected human behaviour, leading
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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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for computer, lab, and fieldwork costs necessary for you to conduct your research. There is also a conference budget of £2,000 and individual Training Budget of £1,000 for specialist training Project Aims and
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for computer, lab, and fieldwork costs necessary for you to conduct your research. There is also a conference budget of £2,000 and individual Training Budget of £1,000 for specialist training Project Aims and
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
generation of wireless communication (6G) to extend network coverage, supporting diverse data-intensive applications such as immersive extended reality and autonomous systems. However, aerial 6G networks will
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lack suitable hardware for data collection in the wild, but our ability to process and understand the resulting data suffers from major constraints. Here, advances in AI will be crucial, for instance by
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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