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crucial insights. In this project, you will contribute to the development of AI-driven methodologies for experimental fluid mechanics , focusing on: Designing multi-fidelity neural networks for adaptive
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to explore the fundamental and applied problems pertaining to binary fluids that exhibit a temperature sensitive miscibility and to investigate the physics of interfacial instabilities when interfacial tension
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of mineralisation. (Caine et al., 1996; Sibson, 1987). Mechanical deformation during faulting reduces grain size, increases surface area, and creates fresh reactive mineral surfaces, potentially enhancing fluid–rock
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EPSRC ReNU+ CDT PhD Studentship: Representative Elementary Volume fluid dynamic modelling for sedimentary geothermal production Award Summary 100% fees covered, and a minimum tax-free annual living
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interactions into account with data-driven method. This research is inherently multidisciplinary, lying at the interface of fluid and solid mechanics, acoustics, and computing science. It will potentially
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discipline. You would be highly motivated and able to work independently as well as collaborate with others with effective written or oral communication skills. Knowledge of fluid mechanics is essential
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for all. This PhD project aims to develop a robust numerical modelling framework to improve understanding and prediction of heat and fluid flow in deep geothermal reservoirs. Using geological, hydrological
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experts in the field. We seek an enthusiastic and talented candidate with a Master’s degree (or equivalent) in Physics or a related discipline. A solid background in quantum mechanics, fluid mechanics
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are available in the School of Mechanical, Medical and Process Engineering at the Queensland University of Technology (QUT, Brisbane, Australia), related to fluid transport processes in porous media. QUT is a
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20 Dec 2025 Job Information Organisation/Company ETH Zürich Research Field Chemistry » Physical chemistry Engineering » Chemical engineering Engineering » Mechanical engineering Physics