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evaluation of modal-decomposition techniques applied to data from high-fidelity numerical simulations of landing-gear aeroacoustics. The researcher will develop and implement modal-decomposition methods using
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emulator of sea ice dynamics, trained using high-fidelity numerical simulations, (ii) variational data assimilation methods, and (iii) a simplified representation of physical processes in the atmospheric
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mechanical engineering, aerospace engineering or a related field. The candidate must have proven experience in numerical simulation in fluid mechanics and/or aeroacoustics; strong background in linear algebra
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candidate must have a proven experience in numerical simulation in fluid mechanics and/or aeroacoustics, as well as strong skills in linear algebra and signal processing. Website for additional job details
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related field. The candidate must demonstrated experience in numerical simulation in fluid mechanics and/or aeroacoustics; strong expertise in linear algebra and signal processing. Website for additional
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FieldOtherEducation LevelPhD or equivalent Skills/Qualifications Skills • Nonlinear mechanics of solids and structures • Formulation of inelastic constitutive laws • Strong numerical simulation and programming skills
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++ and/or Python Experience with SOFA, FEniCSx or similar simulation frameworks is a strong plus Motivation to work at the crossroads of mechanics, AI and medical technology, in close collaboration with
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control algorithms that create value for multiple stakeholders, including EV users, aggregators, grid operators, and energy markets. – Develop advanced co-simulation platforms coupling distribution network