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new thermoelectric materials using data science and machine learning methods applied to materials, based on expert-reviewed experimental data from the literature and public databases (notably
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and with the 2AT team at Institut Pprime to develop a shape-optimisation tool based on resolvent analysis, applied to landing-gear aeroacoustics The researcher will develop a numerical methodology based
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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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finite-difference resolvent solver incorporating stabilising filters as well as a domain-decomposition strategy suitable for complex geometries, - Use efficient time-integration methods to compute
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of the hotspot quantification with the nanoagent size and the optical fluence for instance, and (3) numerically modeling the hotspot measurement with the photoacoustic method to link the mesoscopic measurement
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in process engineering, design of experiments, numerical simulation, thin film characterizations by a combination of structural, chemical and physical methods (XRD, Raman, IRTF, SIMS, XPS, Ellipsometry
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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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methodical, rigorous, reactive, and attentive, with a strong work ethic and high integrity. He/she will already have experience with human stem cells, neuronal differentiation, as well as 3D culture, organoids