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candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods for engineering and will use this experience in collaboration with
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of novel ML/DL methods which leverage both computational and experimental data. Candidates should also have experience of numerical modelling (e.g. CFD and FEA), working as part of a team, with industry and
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authoritative practical knowledge and experience in acoustics and/or vibration, supported by detailed understanding. Basic understanding and knowledge of numerical modelling using the Finite Element Method
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develop new computational methodologies and associated software framework under the guidance of the PI and other collaborators in the project. Your strong working knowledge of numerical methods such as FEM
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and experience in acoustics and/or vibration, supported by detailed understanding. Basic understanding and knowledge of numerical modelling using the Finite Element Method/Boundary Element Method
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number of master’s students. About the PhD project: The PhD positions are part of the project “Physics-Adapted Numerical Methods for Two-Phase Flow” (PANum) funded by the Research Council of Norway
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and Mathematical Sciences. We are seeking a Research Fellow to contribute to a project focused on Quantum magnets, magnon thermal Hall effect and numerical methods. Key Responsibilities: Responsible
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numerical solvers for 2D and 3D phase field models Develop HPC-ready simulation pipelines for large-scale rupture and fracture-fluid systems Optimize performance for modern architectures including GPUs and
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adjustments or a more accessible format to apply for this job online or have any queries about the application process, please contact the HR Administrator. About you A strong candidate will have: A numerate
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numerical flow modeling, with a focus on transient regimes, species transport, and the application of RANS and LES turbulence models, primarily using ANSYS CFX and Fluent; Skills in conducting experimental