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awards at university, faculty and department level, to support both UK and overseas postgraduate researchers applying for competition and self-funded projects. For more information, visit our funding page
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they can reliably, affordably, and fairly support a net-zero energy system. The research will focus on how data-driven and machine-learning-based control can coordinate demand, storage, and local generation
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FFA on different type of metallic surfaces as a function of temperature and concentration. The modelling data and principal component analysis will be used to build property-structural relationships
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performance. This PhD project aims to develop a data-driven framework for graphene aerogel design by integrating structured experimental Design of Experiments (DoE) with machine learning (ML). The student will
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formed during late-stage deglaciation and subsequent marine transgression. These data will provide critical constraints for palaeoclimatic reconstructions and help quantify the magnitude and style
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, and data-driven analysis. The project will be supported by a strong research environment with experience in concrete behaviour at elevated temperatures, constitutive modelling, and advanced numerical
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its frontier by integrating mechanistic artificial intelligence with robotic additive manufacturing systems to enable intelligent metal processing. The research will develop physics-informed and data
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studies, the reactivity of MOFs against ammonia and nitrogen oxides will be examined by EPR. This enables information about metal sites, redox transformations, and possible host-guest interactions
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observable LC configurations for a given physical system; can we design a system to stabilise LC configurations with desired properties and can we reconstruct material properties from experimental data on LC
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waste)? How much of an improvement to air quality could be achieved by improving user behaviour? The student will use this data along with DEFRA commissioned surveys, together with laboratory data and