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to the viscoelastic behavior of polymeric foams under large deformations dynamic loadings. The objective is to simulate, using the finite element method, the response of these porous microstructures under complex
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can generate ultra-relativistic electron beams over centimeter-scale distances thanks to extreme accelerating fields. A key limitation for beam energy is the dephasing between accelerated electrons and
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media where the large proportion of interfaces and the finite amount of matter lead to remarkable properties. The systems studied share the common feature of exhibiting heterogeneities at the nanometric
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to random bifurcations, in which the flow regime switches spontaneously from one type of behavior to another over a random time period. A paradigmatic example is the Von Karman flows, where large scale
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that incorporates a broad range of neutrino and dark-matter models, assessing their effects on large-scale-structure (LSS) statistics as measured by the power spectrum and bispectrum of galaxies or intensity maps
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metals (electrodes), all contained within a vessel. These systems are considered a promising solution for large-scale electricity storage. The aim of the study will be to improve the incorporation
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the possibility of renewal for a third year subject to performance and funding availability. The salary will follow the CNRS/USMB scale and will be commensurate with experience. The anticipated start date is 1
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. AI4GI develops tailor-made neural network architectures, including sparse and biologically informed models, to predict disease risk and complex quantitative traits from large-scale genomic data such as
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processing and synthesizing different types of social and ecological data at various spatial scales, as well as large spatial datasets in R or GIS ■ Experience working with R ■ Experience in socio
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methods to integrate transcriptional and cellular dynamics. Analyze large-scale transcriptomic and spatial dynamics datasets. Work in close collaboration with the team's biologists to test predictions from