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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
. This PhD project will tackle that challenge by developing intelligent methods that combine AI techniques such as language models that interpret technical text and knowledge graphs that map engineering
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-class honours degree or equivalent) in materials science, manufacturing, mechanical engineering, metallurgy, physics, chemistry, or related fields. Ideal candidates will be self-driven, eager to learn CFD
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Engineering, Mechanical Engineering, Aeronautical Engineering, Automotive Engineering or other relevant Engineering and Science subjects, or relevant industrial experience. English language requirements
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Applications are invited for a fully funded, full-time PhD studentship in the Department of Mechanical and Aerospace Engineering, supported by Vestas Technology (UK) Ltd, one of the largest wind
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This PhD opportunity at Cranfield University invites candidates to pioneer research in embedding AI into electronic hardware to enhance security and trustworthiness in safety-critical systems
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | about 2 months ago
This is a fully-funded 4 year PhD offering an annual tax-free stipend of £20,780, tuition fees and an enhanced research and training grant. This PhD is one of a number of projects hosted by
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and PhD students, and communicate your research at national or international conferences. What we are looking for: The successful applicant will have a keen enthusiasm for research in fluid mechanics
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contributing to safer, more efficient next-generation flight. Required qualifications: We are looking for candidates with: A strong background in Mechanical, Materials, or Aerospace Engineering. Experience in
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engineering, advanced characterisations, and computational studies to understand reaction mechanisms for NH3 and NOx conversion. You will have opportunities to collaborate with scientists at the UK and
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, there is no consensus on the adsorption mechanisms of these molecules on the metallic surfaces. In this PhD project we will use state-of-art molecular simulation methods [2,3] to clarify the adsorption and