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on the modelling and optimisation of PRO systems using advanced Computational Fluid Dynamics (CFD) and Machine Learning (ML) techniques. This role offers an exciting opportunity to contribute to cutting-edge
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degree in mechanical, chemical, or energy engineering or similar and experience in some of the following areas: Experience in Multiphysics and CFD modeling involving fluid dynamics, and electrochemical
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to work independently within a dynamic research environment Willingness to collaborate with other research groups Excellent skills in written and spoken English You should strive for scientific excellence
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We are seeking an outstanding candidate for a PhD fellowship in the field of computational fluid and solid mechanics. The fellowship will start on September 1st, 2025, or as soon as possible after
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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of scientific research. Formal qualifications You should hold a relevant degree at the Bachelor’s or Master’s level in computer science, software engineering, information technology, or a similar technical field
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simulation/theory of 2D materials and devices, within electronics, photonics and mass transport. Biophysics and Fluids with a focus on fluid and soft-matter dynamics on small length scales, often with life
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on exploring the evolution of magnesium-based binders in different environments, with particular interest in carbonation mechanisms. You will gain hands-on experience with advanced equipment and computational
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, dynamics and process regulation, process and facility planning, unit operations, heat transmission, fluid mechanics and applied thermodynamics. The Department enjoys very close relations with international