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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
intelligent methods that integrate large language models (LLMs) and knowledge graphs to interpret technical documentation and structure complex engineering knowledge. The goal is to create digital twins
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testing and computational modelling. You'll become part of a diverse, multidisciplinary team that prioritises equity, diversity, and inclusion, gaining specialist expertise in hydrogen-material interactions
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dynamics and tissue morphogenesis during embryo development using cellular, molecular and mechanical approaches. Cell movements underlie tissue patterns and shapes. Using chick embryos as the model system
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assessment. Stress testing & model robustness. Generative imaging models. Please see job description for a full list of requirements. *Candidates who have not yet been officially awarded their PhD will be
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including predictive modelling, computer vision and epidemiology. The student will join an established team of investigators, including statisticians, epidemiologists, image scientists, and clinicians
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treatment processes through advanced machine learning, validated against physics-based models and experimental data. 2. System Integration: Integrating the DTs into material and energy balance equations
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). DETERMIND is the largest cohort of newly diagnosed people with dementia and their carers currently running within the UK. Following the success of the first wave, the programme has received further funding
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models. This theoretical project will facilitate close collaboration with experimental groups and enable benchmarking of theoretical predictions. The PhD researcher will be part of the Correlated Quantum
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maintenance. However, current technologies are relatively slow and not capable enough to provide quick performance, diagnostic and prognostic predictions for real time applications. With the rapid development
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recovery in critical applications, including aerospace, healthcare, and industrial automation. Research Focus Areas: Predictive Analytics for Fault Detection: Develop AI models that predict potential system