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Applications are invited to undertake a three-year PhD programme in partnership with industry to address key challenges in manufacturing engineering. The successful candidate will be based
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to achieve efficient and proactive reconstruction of the printing process, enabling real-time in-situ monitoring of large-volume material deposition and 2) How to adaptively compensate for size-induced defect
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models and physics-based models. More recently, hybrid prognostics approaches have been presented, attempting to leverage the advantages of combining the prognostics models in the aforementioned different
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and modelling techniques. Real-World Impact: Contribute to transformative technologies in clean energy and carbon capture. Future job opportunities: Digital modelling and computational fluid dynamics
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elements like Physical Unclonable Functions (PUFs) and True Random Number Generators (TRNGs) to secure hardware components. Embedded Trust Protocols: Design protocols that establish and maintain trust within
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Verification Tools: Develop AI algorithms that automate the verification process, ensuring systems meet required safety and performance standards. Health Monitoring Algorithms: Implement AI-based monitoring
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, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled
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://www.bris.ac.uk/pg-howtoapply . Please select Mechanical Engineering (PhD) on the Programme Choice page. You will be prompted to enter details of the studentship in the Funding and Research Details sections
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Continuation of award Continuation of the award is subject to satisfactory progress. Number available 3 Funding provider Engineering and Physical Sciences Research Council (EPSRC) Level(s) of study This funding
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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