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, informed by the characteristics of the technology of delivery; • To validate profiles using available demand data; • To integrate barriers and enablers into the model of ASHP installation. Person
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collaboration between industry and academia, now in the final 24 months of a 60-month effort. The programme integrates research and innovation across both physical and digital technologies, to deliver a cyber
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defects without compromising structural integrity, thus ensuring passenger safety and operational efficiency. The project aims to design and prototype a ground-based automated inspection system capable
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PhD Studentship: Optimisation of Liquid Metal Filtration and Cleanliness in Nickel Based Superalloys
A four year PhD with integrated studies is available in the High Temperature Research Centre, School of Metallurgy and Materials under the supervision of Prof Nick Green and Prof Roger Reed, with a
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data analysis, with opportunities to apply your skills to integrating this with other data types (e.g. genomic and transcriptomic). The position is full-time and available for a duration of 4 years
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analysis methods. You will gain expertise in integrating experimental total scattering and high-resolution imaging data with artificial intelligence and atomistic simulation tools to overcome current
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integrate it with energy storage and low-power electronics. The system’s performance will be validated in physiologically relevant in-vitro environments. Throughout the project, the candidate will receive
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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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reconciliation, enhancement, and integration of the MLGB dataset, working with legacy print, manuscript, and digital sources. You will apply and adapt digital methods (especially TEI XML), analyse provenance data
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that support more integrated preventative approaches to complex needs. This interdisciplinary project is ideal for students interested in applied data science, public policy, and improving outcomes