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accuracy is still limited. In contrast, computational fluid dynamics (CFD) models can capture the arc physics and molten pool dynamics, including arc energy transfer and liquid metal convection within
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predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing
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This PhD opportunity at Cranfield University explores how next-generation AI models can be embedded within resource-constrained electronic systems to enable intelligent, real-time performance
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
placement with Rolls-Royce. The research focuses on AI-driven digital twins, using large language models and knowledge graphs for predictive maintenance in aerospace systems. Aerospace systems generate vast
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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project will develop novel methods for modelling and controlling large gossamer satellites (LGSs), so that they can be reliably utilised in space-based solar power (SBSP) applications. The candidate will
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refine simulation tools and machine learning solutions to advance stroke treatment. This involves improving existing computational models that simulate cerebral blood flow, oxygen distribution, and brain
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. The enhanced image quality will support earlier and more reliable detection of eye diseases. Combining artificial intelligence with mathematical modelling, this non-invasive, cost-effective approach has
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estimation for battery management systems for lightweight lithium-sulfur batteries and have specialist expertise in modelling, control and estimation theory, system identification and computer
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physics background. Experience of experimental and computational modelling of icing physics, instrumentation and imaging techniques would be an advantage. Funding The Centre of Propulsion and Thermal