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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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: Coordination Layer: Formulate passivity-based conditions that guarantee agents—modelled as general nonlinear systems—synchronize their outputs or follow desired collective patterns purely through local
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of developing a computational model to simulate hydro-sedimentary dynamics and morphology between restored patches to explore conditions favourable for restoration. Validated with data on vegetation cover and
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of the infrastructure, design and execution of large‑scale measurement campaigns, and development of data‑driven models for room acoustics and spatial‑audio. The specific research direction will be finalised after
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the solidarity and cooperation within and between democratic countries. There have been concerns about the use of Generative AI and Large Language Models (LLMs) as tools for the dissemination of disinformation and
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challenges in quantum technology adoption stem from the lack of standardized benchmarking methods and the inherent difficulty in validating quantum devices beyond classical simulation capabilities. Recent
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, this interdisciplinary project will couple mathematical models of earthworm movement, stochastic models of the measurement process and designed experiments to improve earthworm detection. Project This project will work
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-frequency Joule losses. Litz wire is one of the most promising solutions due to its exceptional ability to reduce AC losses and boost power density. Today's modelling tools are not yet equipped to fully
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will generate a wide range of contemporary high-resolution next-generation sequencing and array-based genomic and epigenomic datasets across large cohorts of human tumours and experimental models
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alongside numerical simulations relying on high-performance computing and reduced order modelling. We aim to gain new insights about the physical coherent structures which are most relevant to viscoelastic