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. This project will develop and apply new computational/analytical tools to guide XFEL experiments for specifically tracking lattice fluctuations and ion dynamics in energy materials (batteries). The project will
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create a computational tool based on experimental input, simulated data, and machine learning methodology to extract 3D atomic structure information from 2D identical location STEM images. STEM image data
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exceptional environment for students from physical sciences, life sciences, mathematics, statistics, and engineering. HetSys specializes in applying advanced mathematical methods to tackle complex, real-world
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testing and computational modelling. You'll become part of a diverse, multidisciplinary team that prioritises equity, diversity, and inclusion, gaining specialist expertise in hydrogen-material interactions
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, in collaboration with Rolls-Royce, will develop innovative coatings to safely contain hydrogen in critical aerospace materials through experimental and computational modelling work. You’ll join a
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, computational modelling and experimental work. You’ll join a pioneering multidisciplinary team that values equity, diversity, and inclusion, gaining unique expertise in turbomachinery pump development, hydrogen
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Type of award Studentship Managing department Department of Mathematics Value This fully-funded studentship includes: Tuition fees at home or international rate Annual maintenance stipend equivalent
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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Programme: Hybrid CFD and process simulation for process intensification of post-combustion CO2 capture School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof
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of big data might not be possible to be captured by traditional modelling approaches. This implies that mathematical modelling of such data is infeasible. The data-driven modelling approach could resolve