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overseas. Training can be provided in computational fluid dynamics, machine learning, and nonlinear dynamics. These skills are highly valued across a wide range of industries. Recent data reveals that Fluid
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-state physics, fluid dynamics, solid-dynamics, and fracture/degradation; all in a highly transient and non-linear system. In this project we will extend multi-component, multi-phase field frameworks
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experience in computational modelling. It will involve the use of open-source computational fluid dynamics codes, with turbulence modelling and porous media approaches. It will also require the development
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formation. Complementing these experimental efforts, Computational Fluid Dynamics (CFD) simulation will be employed to interpret CRUD build-up measurements, identify key phenomena influencing CRUD deposition