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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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, development of dynamic physical models that allow counterfactual reasoning of highly complex systems such as plane engines, automated robotics that allow fully autonomous exploration and modelling of unknown
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date is 1st October 2023. The PhD student will be physically located at Cranfield University, in the Centre for Electronic Warfare Information and Cyber at Cranfield Defence and Security (CDS). CDS
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developed Physics-informed Neural Network (PINN) technique will be first explored, tailored, and extended into the PdM context of high-value critical assets. It is expected that combining the domain knowledge
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fitting for reduced order electrochemical models. Early detection of thermal anomalies in battery packs. Physics-based models and state of health estimation in lithium-sulfur batteries. Collecting data and
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rapid source noise prediction of integrated helicopter-engine systems. The research will be sponsored by the Engineering and Physical Sciences Research Council (EPSRC) and the Defence and Science
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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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-efficient research that prevents fatigue failures has pushed towards integrated computational materials engineering approaches that improve competitiveness. These approaches rely on physics-based models
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that form part of the Cranfield Defence and Security (CDS) Faculty. It is located on the edge of England’s beautiful Cotswold region, just an hour West of London. The application process is uncomplicated