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Field
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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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Engineering, Faculty of Engineering and Applied Sciences, Cranfield University, in the area of performance simulation, analysis, and optimization of supercritical CO2 power generation systems. Cranfield has
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Funding amount Standard UKRI stipend, RTSG, Fees Covered. The project: Tracking and controlling the spread of emerging pathogens using sequentially collected data is a critical challenge in public
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over conventional single-rotor designs. However, MR technology is still in its infancy, and hence many interesting research and engineering questions remain to be answered to demonstrate the true
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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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for candidates with a background in meteorology, climatology, physics, engineering and any related discipline, and a strong interest in applying advanced physical and computational methods to real-world
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technology increases the grid’s exposure to cyber-attacks, which can compromise measurement signals, disrupt control commands, or induce model or data-driven instability. This project aims to develop a robust multi
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, interpretable models from experimental and operational data. The core goal is to balance model accuracy with computational efficiency, while meeting the needs of experimental validation. The framework will
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capture, evaluate the potential apportionment of soil/sediment chemistry to sources and locations from established field experimental plots and catchments-valleys identified from baseline data using isotope
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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