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sustainable engineering strategies to boost their performance to deliver benefits for the environment and society. The outputs of this work will support practitioners in making informed investment decisions
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-on experience with real-world SCADA data, industry collaboration with RES Group, and training in high-fidelity simulation environments (OpenFAST, Digital Twin technology). This opportunity is ideal for those
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this testbed available to users for testing hardware and applications. NPL will lead on the testing and security evaluation of the testbed and collaborate widely on the technology development. The student’s
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This is a self-funded PhD position to work with Dr Adnan Syed in the Surface Engineering and Precision Centre. The PhD project will focus studying high temperature corrosion mechanisms in details
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microenvironment structures are associated with genomic features and clinical outcome. Danenberg E et al. Nat Genet. 2022 May;54(5):660-669. doi: 10.1038/s41588-022-01041-y Imaging mass cytometry and multiplatform
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Fully Funded PhD Studentship (Home) in 4D Printing for Advanced Bioelectronics Contact: Supervisor: Dr Xiaolong Chen (Xiaolong.chen@nottingham.ac.uk) Faculty of Engineering, M3 Department, Advanced
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infrastructure components, causing potential reliability, safety, and longevity issues. Addressing this critical issue represents a vital area of research in aerospace materials science and hydrogen engineering
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable
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the second phase particles and recrystallization. The project aims to understand the complex interactions between alloy chemistry, processing, microstructure and performance. The alloy microstructure will be
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the development of specialized hardware architectures capable of efficient, real-time processing. Embedded AI hardware architectures, including neuromorphic processors and low-power AI accelerators