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industries where property gradients are required; these will include automotive, aerospace, and chemical industries. Our method is iterative and data-driven, with a strong emphasis on optimisation
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other members of the study team (e.g. the clinical research fellow, research nurses, the statistician, the data manager and clinical studies associate) to ensure the implementation of all aspects
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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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experimental manufacturing data. Thermoplastic overmoulding is an advanced composites manufacturing process combining stamp forming of aligned, continuous fibre composites (CFCs) and injection moulding
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applicable in real time or without availability of case-specific data in terms of machine types. To address such problems, recent works have proposed machine learning techniques and data which can be easily
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characteristics of devices. Data analyses will include device simulations and comparisons with other technologies. In addition, there will be device fabrication activities to produce suitable samples
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and control lofting through adjustment of weaving and compaction parameters. Create a process monitoring system to integrate data from the loom with the resulting woven structure. Develop a reduced
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experimental work, undertake data analysis, prepare results for publication and communicate findings in workshops and conferences. This appointment will be subject to advanced pre-employment screening (via
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for designing everything from longer-lasting batteries to more effective medicines. However, a major roadblock exists: understanding the complex data from these experiments is a slow, manual process that can
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progress and attendance data to ensure that students meet milestones on time, reporting data to the Faculty Doctoral School Manager. Support the PGR application process, working closely with central