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project’s focus is to: Conduct cutting-edge experiments to investigate how surface texture affects seal performance and explore the use of an ultrasonic sensor for real-time monitoring. Experiment with
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property distributions from process-induced microstructure variations using the ML models created. Work with the extended team to link the simulation of sensor data with new multi-scale processes
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in reviewing, testing, refining, and providing feedback on historical records that are automatically transcribed, coded, and linked using computer algorithms. Support the project and technical team in
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undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield. Structural Health Monitoring (SHM) is the process of using real-time sensor data from high-value engineering assets to inform
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Novel glass-based sensor technologies for wide-range, radiation hard sensing applications in nuclear fusion applications (Associate University project at Sheffield Hallam University)
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Novel glass-based sensor technologies for wide-range, radiation hard sensing applications in nuclear fusion applications (Associate University project at Sheffield Hallam University) The Fusion
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haptic guidance methods that respond to operator skill levels. Identify trajectory features that characterise expert performance for training robots. Develop algorithms that allow robots to refine
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also be joining the Leonardo Centre for Tribology, which is an active and friendly group. There are ~25 PhD students working on machine elements, tribology, lubrication, and sensor systems for wind, auto
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deep learning algorithms in ESRI ArcGIS or similar software. Desirable Application Proficiency with relevant specialised software and approaches (e.g., geographic information systems, high-performance
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modelling of selected zones within SCH to investigate the extent of overheating. This involves providing support for installation of environmental sensors in selected SCH zones to monitor indoor conditions