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the simulation results. This work builds on our previous research on crosslinking at the substrate/polymer interface: Suzanne Morsch, Yanwen Liu, Kieran Harris, Flor R. Siperstein, Claudio Di Lullo, Peter Visser
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The Project: Are you passionate about materials science, numerical modelling and materials characterisation techniques? Do you want to work on cutting-edge research that will directly impact nuclear
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-efficient research that prevents fatigue failures has pushed towards integrated computational materials engineering approaches that improve competitiveness. These approaches rely on physics-based models
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quantification for greater accuracy and reliability. Material failure is a critical area of study in engineering, as understanding how materials behave under stress is essential for designing safe and reliable
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research projects across areas such as: Zero Emission Technologies. Ultra Efficient Aircraft, Propulsion, Aerodynamics, Structures and Systems. Aerospace Materials, Manufacturing, and Life Cycle Analysis
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Application deadline: All year round Research theme: Multi-scale modelling How to apply:uom.link/pgr-apply-2425 No. of positions: 1 This 3.5 year PhD project is fully funded for home students
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that are not seen in any other material. This project combines cutting-edge sampling techniques with machine-learned potentials for accurate phase predictions, offering considerable opportunity for method
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material sticks to the edges of the drier, causing wastage of the product, and in extreme cases, deterioration of the drier itself. This research aims to develop a new multiphysics model to describe
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. While a background in materials science is preferable, it is not a prerequisite, as there are opportunities for retraining. This ensures that the project is accessible to individuals from diverse academic
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Supervisory Team: Prof Middleton, Prof Altamirano PhD Supervisor: Matt Middleton Project description: Black holes grow by accreting material through a disc which is bright across the EM spectrum