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to the additive manufacture of an aluminium alloy. Develop melt pool physics simulations of the additive manufacturing process for the industry-based aluminium alloy. Develop computational methods for quantifying
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performance of the materials. A modelling platform will be developed in this project where the effect of various compositions of additives on CFRP properties will be simulated using numerical methods
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computing Numerical simulations Modelling Data analytics Numerical optimisation Fluid dynamics Entry Requirements If you have received a First-class Honours degree, or a 2:1 Honours degree and a Masters, or a
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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse
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this project you will develop and test a range of these inhibitory peptides, based on prototypes already developed in the lab. In parallel you will develop a recombinant method of peptide production that will
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flares using the assumption of hydrogen recombination continuum emission, in place of a blackbody estimate. In parallel, but not in collaboration, Pietrow, Cretegnier, Druett et al. (2024) have advanced
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these methods for thin coated membranes. As part of a team, you will develop mathematical and computational models, as well as lead the experimental work. You will work closely with our industrial partner. You
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capabilities, remains limited. The unprecedented design freedom offered by AM beautifully harmonises with topology and layout optimisation methods - well-developed techniques revealing elegant and high
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industry experts, gaining valuable skills at the interface of hydrogeology, environmental engineering, and computational modelling. Research Objectives will focus on: (a) Enhancing CoronaScreen by
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aerospace structures. We plan to adapt these methods for thin coated membranes. As part of a team, you will develop mathematical and computational models, as well as lead the experimental work. You will work