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Field
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prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs
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& further information Informal queries: Project page: www.tcd.ie/mecheng/research/fluids-acoustics--vibration/projects/noise-2050 Shape Ireland’s soundscape of the future - apply now and make your research
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degree in Hydraulic Engineering, Hydropower Engineering, Civil Engineering, Fluid Mechanics or equivalent. Your course of study must correspond to a five-year Norwegian course, where 120 credits have been
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element modeling, computational fluid dynamics). Knowledge of heat and mass transport processes in heat-sensitive materials and process optimization. Experience in supply chains and hygrothermal
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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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development of special phase field and phase field crystal models coupling newly developed approaches with established approaches for simulating e.g. mechanical properties and the flow of fluids implementation
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the role of stress-controlled structural permeability in hydrogen migration. The project will also investigate the petrophysical and fluid-flow properties of fault rocks and interrogate paleo-episodes
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(for plasma catalysis). Computational fluid dynamics & kinetic modelling of plasma reactor design. You will publish scientific articles related to the research project. You will carry out a limited number of
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partners in the European project, in particular also with the research partner at the Royal Military Academy in Belgium, who is doing the Computational Fluid Dynamics (CFD) simulations to estimate
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of the highest quality within building design and processes, building construction and safety, building energy and services, solid mechanics, fluid mechanics, materials technology, manufacturing engineering