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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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regions, and may have also been observed in historical trends, but the processes driving this delay are not well understood. This project will use observations and climate model simulations to examine how
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REF2029 impact. Methods and workplan Data and simulation: curate multi-fidelity datasets that couple finite-element heat simulations with active thermography experiments. Model design: extend FNOs with
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ecosystems, particularly intertidal and shallow subtidal zones, are highly dynamic and thermally extreme, exposing organisms to both marine (MHW) and atmospheric (AHW) heatwaves, often in rapid succession
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will use a high-fidelity large eddy simulation (LES) code and scientific machine learning tools, such as real-time optimisers, in order to simulate wind farms exposed to various atmospheric inflows. Some
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performance of AI models for fall detection. The research will combine experimental studies on different floor systems, finite element simulations of vibration propagation, and AI-based signal analysis
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of barrier winds off East Greenland using new wintertime observations from a research cruise. Carry out numerical weather prediction simulations of barrier wind case studies with the observed sea-ice
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to reinforced concrete slabs, each with unique stiffness, damping, and boundary conditions. These structural differences fundamentally shape how vibration signals are transmitted and recorded. This project will
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the biological mechanisms behind these neurological and sensory disorders and harness this knowledge to develop new therapeutic strategies. We have world-leading experts who interrogate these conditions at
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, damp, ventilations). We will use these simulations to propose real-life measures to improve living and working conditions. Such measures might be recommendations on how to design or retrofit indoor