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Rolls-Royce University Technology Centre (UTC) in manufacturing and On-Wing Technology, The University of Nottingham. Applicants are invited to undertake a three-year PhD programme in partnership
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A position exists, for a Research Assistant/Associate in the Department of Engineering, to work on Data Science for Construction Productivity. The researcher's responsibilities will include
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deep learning methods to enhance the predictions beyond existing data. By incorporating microstructural features into predictive models, the aim is to create a reliable data-driven modelling framework
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energy data. The skills they will develop will be highly sought after both in university research on energy modelling and as a practising engineer in industry. The profitability and sustainability of many
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comprehensive analysis of the extensive Pulse dataset, uncovering latent patterns and taxonomies that define building leakage characteristics. Surrogate Model Development: You will develop data-driven surrogate
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invites applications from candidates with a robust foundation in data science, modelling, and/or engineering, and a keen interest in deploying data analysis and artificial intelligence (AI) to solve real
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the PhD student in high-performance computing, computer programming, applied mathematics, fluid mechanics, mathematical modelling and data analysis for large datasets -of the order of 100 Terabytes
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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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their practical deployment. The Project: This PhD will develop the science and engineering required to overcome these bottlenecks, with the following objectives: • Uncover the mechanisms driving enhanced hydrogen
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renewable energy, AI-driven engineering, and industrial research. Cranfield’s expertise in wind energy systems, predictive maintenance, and AI applications provides an ideal environment for cutting-edge