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sound background in geophysical fluid dynamics, experience in numerical ocean or atmospheric modelling, and experience with numerical data analysis. Good scientific presentation, writing, and
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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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understanding of fluid dynamics and transport phenomena, along with motivation for research and scientific writing, is essential.Qualifications1) Must hold Bachelor’s degree for Master positions (CGPA > 3.3)2
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development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria A knowledge of fluid dynamics is recommended. Personal
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by using commercial software such as Ansys, Abaqus, SolidWorks, etc. Experience in computational fluid dynamics (CFD) modelling or finite element (FE) modelling; Fundamental knowledge in fluid
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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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capture technologies. In this project, you will: Develop a 3D Digital Model: Create an advanced computational model of high-pressure mechanical seals. Apply Computational Fluid Dynamics (CFD): Simulate gas
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Application deadline: All year round Research theme: Applied Mathematics, Mechanical and Aerospace Engineering, Fluid Dynamics How to apply:uom.link/pgr-apply-2425 How many positions: 1 This 3.5
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Fully-funded PhD Studentship: Adaptive Mesh Refinement for More Efficient Predictions of Wall Boiling Bubble Dynamics This exciting opportunity is based within the Fluids and Thermal Engineering
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overcomes the geographic limitations of conventional systems, enabling global scalability and accessibility. Using advanced computational fluid dynamics (CFD) approaches, the project is aimed at advancing