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Field
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Modelling of Weather Impacts: Build machine learning models that analyse these integrated data streams to identify early precursors of weather-induced disruptions. The goal is to forecast turbulence zones
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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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motivated PhD candidate with interests and skills in computational modelling and simulations, fluid dynamics, mechanical engineering, physics and applied mathematics. You should have experience in one or more
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, encompassing advanced geospatial analysis, remote sensing methods, atmospheric transport modelling, and epidemiological data integration. The researcher will also receive guidance in handling large datasets
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
-latency, and scalable operation in aerial 6G networks. In this regard, Large Language Models (LLMs) have recently emerged as a key technology to achieve adaptive 6G spectrum management. The core idea of LLM
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pilots to real-world systems. The overarching aim is to deliver a scalable approach, pairing shared “aggregator” models with household-specific “client” models that exchange knowledge while keeping data
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research. The student will work closely with colleagues in the topical area of Atmospheric and Climate Physics (ACP). The successful candidate will have opportunities to present their work at international
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modelling for the sources of fine particulate matter (PM2.5) at urban sites in the UK’, Atmospheric Environment, 343, p. 120963. Defra Air Quality Expert Group (2017) The potential air quality impacts from
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overheating models by integrating TIR imagery with energy flux data, building physics parameters, and local weather conditions. Apply machine learning techniques for TIR and other open-source image analysis
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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