48 postdoc-in-thermal-network-of-the-physical-building PhD positions at Cranfield University
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to industrial clients such as Boeing, BAE Systems, Rolls-Royce, Meggitt, Thales, MOD, Bombardier, QinetiQ, Thales, Network Rail, Schlumberger and Alstom. Entry requirements A minimum of a 2:1 first degree in a
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and nanomaterials at the Composites and Advanced Materials Centre (Dr Sameer Rahatekar, Prof Krzysztof Koziol) and Hyper-velocity impact testing facilities at Centre for Defence Engineering and Physical
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable
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of representative failure models for gear failures causes difficulties in their useful lifetime prediction. Critical operational parameters such as loading, speed and lubrication affect the physics of gear meshing
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A funded PhD studentship is available within the Autonomous and Cyber Physical Systems Centre at Cranfield University, Bedfordshire, UK. As aerospace platforms go through their service life, gradual
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Centre is providing its state of the art academic and research services to industrial clients such as Boeing, BAE Systems, Rolls-Royce, Meggitt, Thales, MOD, Bombardier, QinetiQ, Thales, Network Rail
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for a Condition-based maintenance (CBM) which holds the promise of predicting machinery maintenance requirements based on process performance measurements. Diagnostics and Prognostics are essential