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Location: Mechanical and Aerospace Systems Research Group, Faculty of Engineering, University of Nottingham Funding: UK Home fees + tax-free stipend of up to £25,000 p.a. for 4 years Applications
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respectively, we seek highly motivated candidates to develop innovative methodologies for radar sounder data processing, with a strong emphasis on artificial intelligence and deep learning. The research activity
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-learning tools. This project will prepare you for starting a career in nuclear decommissioning or applying emerging technological and modelling approaches to facilitate circular economy innovation in
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to establish improved, AI-enhanced calibration strategies for diverse industrial and geophysical materials. Ultimately, it seeks to determine the optimal, AI-informed approach for selecting and calibrating
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to learn are essential. Essential: A first-class undergraduate degree or a master’s degree in Physics, Applied Physics, Electrical and Electronic Engineering, Mathematical Sciences, or a closely related
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components might be only practically verified using one verification method. For example, a machine learning vision component cannot be realistically formally verified but it can undergo a rigorous testing
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would be helpful, but these are not essential. The project is well suited to candidates from earth science, geography, engineering, or related backgrounds who are motivated to learn new technical skills
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PhD Studentship in Aeronautics: How offshore wind farms and clouds interact: Maximising performance with scientific machine learning (AE0078v2) Start: Between 1 October 2026 and 1 July 2027
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) are widely used in industries such as automotive and electrical engineering. During service, these materials are frequently exposed to harsh environments involving elevated temperatures, oxygen, and moisture
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inspection technologies with techno-economic modelling to enable condition-based circular economy decision-making for offshore energy infrastructure. The project will: (1) evaluate autonomous inspection