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for resilient water resource management. The research will explore three core dimensions: transboundary water dynamics, institutional and policy effectiveness, and the impacts of climate variability on urban
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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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language understanding (to interpret instructions), and action generation (to respond), enabling robots to perceive, reason and act flexibly. The models will be trained on simulated datasets to learn general behaviours
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to perceive, reason and act flexibly. The models will be trained on simulated datasets to learn general behaviours, then fine-tuned for specific tasks. The aim is to create intelligent agents that could
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, finite element simulations of vibration propagation, and AI-based signal analysis to establish a physics-informed understanding of the relationship between structural behaviour and fall detection accuracy
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supportive, inclusive research environment; opportunities to co-create your project; hands-on fieldwork and wildfire simulation experience; collaboration with leading researchers; and training in advanced
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flood extents under different storm surge scenarios, as determined through high-fidelity CFD-DEM simulations? - What is the optimal spatial arrangement (single/multiple lines, angle of incidence) of PBs
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operational literature and interviews with key stakeholders, including the Environment Agency and local communities. 2. Systems Dynamics Mapping and Modelling: This will inform a conceptual 'systems-of-systems
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weather-related extremes, much of it by our team, however we must also consider their cyber risks. Network modelling will be used to simulate a range of different cyber and natural hazards threats