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self-locking mechanisms for occupational exoskeletons FEM analysis on mechanical components to evaluate stress, strains and deformations Fluid dynamics modeling and definition of fluid composition
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, mechanics, fluid dynamics, or physics Excellent technical understanding and analytical capabilities High commitment and motivation, willingness to contribute to academic teaching Team spirit, strong
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closely with experts in fluid engineering, the Medical School, and Sheffield Teaching Hospitals. As a key member of this interdisciplinary project, you will lead the theoretical and computational study of a
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through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The successful candidate will join a
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skills : Interdisciplinary collaboration (fluid dynamics + acoustics + scientific computing) Analytical thinking Scientific curiosity Resilience (handling failed simulations/experiments) Initiative
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 1 hour ago
and aerodynamics through the use of comprehensive analysis, high-order computational fluid dynamics (CFD), and direct numerical simulation (DNS) tools. The postdoctoral researcher will work with CAMRAD
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Institut National des Sciences Appliquées de Lyon | Villeurbanne, Rhone Alpes | France | 16 days ago
, the programme aims to accelerate the transfer of ABH technologies towards real-world applications in sectors such as aerospace and energy. PhD project : Vibroacoustic control of fluid-filled pipes with add-on
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-boundary problems has been developed during last decades as a consequence of problems solved in physical or biological contexts, achieved advances in material science, space technology and fluid dynamics
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skills, both verbal and written, are required. Key duties: Successful candidate is expected to: Setup, validation, and evaluation of Computational Fluid Dynamic (CFD) models for the elucidation
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well as interpretability of scientific foundation models. The rush to build foundation models has led to the development of large machine learning models in Astrophysics, fluid dynamics, biology, weather prediction, solar