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AI systems for real-time 3D mapping on compact, low-power devices. The project will combine optical sensing, event-based vision, and radio-frequency (RF) data with advanced AI to build robust mapping
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finite element analysis; MATLAB or Python programming; An interest in electrified propulsion and industrially relevant research. Experience in electromagnetic design, thermal analysis, optimisation
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consistency, and user behaviour, incorporating insights from survey data and textual analysis. By integrating technical and socio-economic perspectives, the project aims to develop new strategies
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offers an exciting opportunity to explore ultrathin metamaterials: a novel type of device that utilises digital and mathematical techniques to design multifunctional visual aids to help address and correct
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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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properties. The interaction between fuel chemistry and physical behaviour will be investigated. If appropriate experimental analysis to provide validation data will be acquired as part of the PhD, although
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under UK climate and grid demand profiles, economic assessment, life‑cycle analysis, and AI‑driven multi‑objective optimisation. Although the initial focus will be on gas turbines, the hybridisation
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([email protected] ) b. Submit a short Faraday Institution expression of interest form: https://www.surveymonkey.com/r/XL23CSF c. Apply through the University of Nottingham’s application portal
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that deliver healthier indoor environments, lower carbon emissions, and long-term building performance. By integrating Passive House and EnerPHit principles with real building data, the research will support the
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Studentship Information Supervisor: Prof Rumiana Ray (University of Nottingham) Secondary Supervisor: Dr Hadrien Peyret (University of Nottingham), Dr Dong-Hyun Kim (University of Nottingham), Prof