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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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to populations and infrastructure. While heavy rainfall is well recognized as a triggering factor, recent studies have revealed the importance of snow cover and, in particular, rapid melting dynamics in
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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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scientific activities, such as automatic and DFT methods. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR5256-SEBLEV-001/Candidater.aspx Requirements Research FieldChemistryEducation
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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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different methods of analysis used in the community, in particular linguistic probes (classifiers trained to predict certain linguistic properties from representations discovered by neural networks
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. Research within LIG is organized into 5 focus areas: Intelligent Systems for Bridging Data, Knowledge and Humans, Software and Information System Engineering, Formal Methods, Models, and Languages
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AI researchers from ANITI, IMT and CERFACS, as well as with researchers/engineers in weather forecastings from the CNRM (Météo-France). Hybridization methods between neural networks and physical models
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of natural language texts. This PhD position aims at investigating new computational methods to reduce distortions in science communication. The envisioned methodology will focus on Argument Mining with a
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on the complementary expertise and recognized excellence of its 22 research teams to contribute to the development of the fundamental aspects of computer science (models, languages, methods, algorithms) and to foster