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Field
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Overview: As data becomes more accessible, new challenges arise around how best to use it—especially in complex, multi-system environments like aerospace. Ontologies offer a powerful solution by
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
intelligent reasoning and feedback mechanisms into digital twin environments, enabling them to interpret complex maintenance data more effectively. Using AI techniques, such as large language models, knowledge
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problem-solving skills and deep expertise in the development of complex computational models. Candidates who have not yet acquired their PhD would be appointed at the Research Assistant level. The
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Overview: Project Overview Investment casting is an ancient yet vital manufacturing technique, especially in the aerospace sector where thin-walled, complex components are increasingly required
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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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About the role The Department of Mathematics at the University of Sussex is inviting applications for a fixed term early career research position centred on advancing ecological modelling of soil
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, osteosarcoma) cell and time of origin is debated but experimental studies in different models suggest possible in utero induction with postnatal initiation similar to leukaemia. In Ewing sarcoma, FET::ETS gene
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(complexity). Develop and optimise a modelling pipeline including a decision support dashboard for optimal patient selection for surgery to ensure daily surgery caseload optimisation, post-operative care
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the risk of missed defects. Using the power of Artificial Intelligence (AI), this research aims to: Automate defect detection in complex 3D structural data Enhance diagnostic accuracy and processing speed
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is characterised by complex and highly dynamic turbulent flows that define the performance and design of renewable energy systems and their infrastructure. This PhD project aims to enhance