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details This is an outstanding development opportunity for a talented individual who wishes to conduct research on children and young people with life limiting conditions and their families. Previous
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, therefore there is a need for collaboration among different stakeholders to properly develop the optimisation methodology. Furthermore, it is vital to incorporate the impact of any design modification
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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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development through the provision of training, continued support, and career progression opportunities You will have access to a range of benefits and rewards, including fitness and health facilities, staff
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This self-funded PhD research project aims to develop smart sensors based on low-frequency resonance accelerometers for condition monitoring of ultra-speed bearings. The developed smart sensors will
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for a pre-doctoral academic with an interest in primary care cancer diagnosis and will lead to skills development particularly in analysis of observational data and may lead to opportunities to employ
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project at the CNNP Lab . The aim of ACORN is to understand the role of biological rhythms (chronobiology) in epilepsy and develop time-adaptive therapies. This project is funded by UKRI and is a
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to talented prospective or mid-study students towards their PhD studies in Research and Development Management. Tenable period Up to three years (pro-rata for part-time students) Continuation of award
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. The successful candidate for this PhD position will be involved in the experimental and theoretical development of a new technology called Surface Nanoscale Axial Photonics (SNAP), which will enable
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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field