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We invite applications for a fully funded PhD position in the field of numerical modelling of iron electrodeposition, i.e., multiphase flows involving phase change, using fully resolved CFD methods
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dispersion. Yet conventional Computational Fluid Dynamics (CFD) workflows rely on watertight geometric models and volumetric meshes that are slow, complex, and costly to produce. Within the POINT-TWINS project
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make extensive use of low-fidelity simulations which can provide fast but inaccurate solutions depending on the flow complexity. To close this gap, this PhD will explore machine-learning (ML) methods
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, this PhD will explore machine-learning (ML) methods to significantly reduce the turnaround time of SRS, thus enabling their use for industrial design processes. By combining state-of-the-art numerical