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Are you an innovative researcher with a strong background in CFD, scientific machine learning (ML), wind energy, and advanced optimization? Join our team to develop cutting-edge solutions
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you an innovative researcher with a strong background in CFD
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The Multiphysics Computations Section at Argonne National Laboratory is seeking to hire a postdoctoral appointee for performing high-fidelity scale-resolving computational fluid dynamics (CFD
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behaviour of the RPB using a CFD approach. - Implement an RPB for solvent regeneration. Where to apply E-mail michel.meyer@ensiacet.fr Requirements Research FieldEngineering » Chemical engineeringEducation
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publications in archival and conference presentations. Desired Experience: Professional expertise with a focus on applying advanced CFD and post-process tools to the fields of computational aeroacoustics. Create
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ocean environments, ensure safe and sustainable operations. Our activities are centered on numerical modelling (e.g. CFD, FEA, FSI, optimization, machine learning), but also include experiments and real
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: Experience in one more of the following areas: Mathematical methods for computational fluid dynamics (CFD) Finite elements methods Modern machine learning software tools and frameworks Implementation
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candidate will play a key role in developing and advancing new models and simulations for Computational Fluid Dynamics (CFD) hypersonic codes. Specific tasks include developing new turbulence and transition
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will be carried out using OpenFOAM, an open-source CFD software with turbulence, bi-fluid, and non-Newtonian modeling capabilities. Alternative equivalent software known to the candidate may also be used
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Science, or a related field completed within the last 5 years Preferred Qualifications: Experience in one more of the following areas: Mathematical methods for computational fluid dynamics (CFD) Finite