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to the complexity of the mathematical models that describe them. The current consensus is that there are three “types” of viscoelastic chaos: modified Newtonian turbulence, elastic turbulence, and elasto-inertial
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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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skills include: Interest or background in composite materials, particularly in modelling and/or testing Basic understanding of finite element methods (FEM); any exposure to impact or burst mechanics is a
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materials, particularly in modelling and/or testing Basic understanding of finite element methods (FEM); any exposure to impact or burst mechanics is a plus Familiarity with FE simulation tools such as ANSYS
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spectroscopy and HPLC), organic synthesis, electrochemistry, hydrogel soft matter and modelling. You will be mentored by Dr. Maguire and will learn how to design and manage projects, how to conduct research, how
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The project: The deployment of generative AI—particularly Large Language Models (LLMs) based on transformer architectures—in industrial settings poses several critical challenges. Ensuring reliable
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. ii) to use mathematical modelling methods to determine how on farm management practices can impact the carbon footprint of farm carbon assessment. iii) to contribute to the body of scientific
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join a vibrant, supportive research community (around 20-25 people involved in fluids modelling research). Collaborate with the Leonardo Centre for Tribology: Work with top researchers on experimental
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Exciting Fully Funded PhD: Computational Modelling for High-Pressure, Low-Carbon Storage Technologies. Be a Key Player in Shaping the Future of Clean Energy Storage! School of Mechanical, Aerospace
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benchmarking (Frank et al., arXiv:2024) using blind testing approaches. The student will develop verification-based benchmarking approaches for digital quantum computers with realistic noise models, leading