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Materials Experience thinking with biosensing, together with a toolkit, including a sensor-based system, for studying human-material experiences with temporal and biofabricated textiles. You will then apply
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following areas: Virtual, Augmented, and Mixed Reality (VR/AR/MR) Cognitive Modeling and Decision Making Motor Learning Artificial Intelligence and Machine Learning Wearable Technologies and sensors Human
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following areas: Virtual, Augmented, and Mixed Reality (VR/AR/MR) Cognitive Modeling and Decision Making Motor Learning Artificial Intelligence and Machine Learning Wearable Technologies and sensors Human
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-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which
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grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations
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sensor measurements to embankment responses will be derived and validated using field data. These relationships will provide a framework for translating routinely collected monitoring data into actionable
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study. The research will begin with the analysis of baseline and historical data collected from wayside sensors installed on selected instrumented railway bridges. Building on these insights, an on-board
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-induced dynamic behaviour. Transfer functions linking LDV, ABA, TG, and wayside sensor measurements to embankment responses will be derived and validated using field data. These relationships will provide a
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of railway tracks on bridges, using Dutch railway infrastructure as a primary case study. The research will begin with the analysis of baseline and historical data collected from wayside sensors installed
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sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits