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in decarbonising heat especially in rural, off-gas communities reliant on oil, LPG, and inefficient electric systems. While electrification remains a key pathway, limited grid capacity across
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preliminary receiver positions. Despite these advances, current literature provides little guidance on how to systematically incorporate domain-specific constraints into the training and inference
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. Signal Conditioning: Embedding low-power electronics for amplifying and encoding the DA oxidation current into a transmittable signal. Wireless Data Transmission: Establishing reliable signal communication
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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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expand current technology to include automated live analysis, integrating machine learning algorithms capable of interpreting the complex behavioural patterns of mussels in response to environmental stress
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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
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Photovoltaic (PV) power is crucial for advancing global energy sustainability, as it harnesses abundant and renewable solar energy to generate clean electricity with minimal environmental impact. By
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, durability, and environmental sustainability, while addressing cost constraints and net zero objectives. It will include an in-depth review of shortcomings in current design, based on literature review and