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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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practice of English (level B1 desired) • In-depth knowledge of the Python language • Autonomy, interpersonal skills and ability to work in a team Website for additional job details https://emploi.cnrs.fr
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research environment involving several PhD students and postdocs, and strong regional and international connections, with collaborations with the University of Geneva and CERN. Terms of Employment
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of data, services, interaction devices, and use cases influence the evolution of software and systems to guarantee the essential properties such as reliability, performance, autonomy, and adaptability
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its mechanical deformation and response to atmospheric conditions, which remain a major challenge for existing models. The use of neural emulators and differentiable programming techniques opens up new
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using neural networks (DNNs). The PhD work will focus in particular on the prediction of extreme weather events and will use meteorological data bases provided by Météo-France [py4cast, 2025; Anemoi 2024
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Starrydata2). The work will include the implementation of machine learning models (neural networks, random forests, SISSO), generative approaches for predicting crystal structures, the use of machine learning
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oxidized materials, demands the development of new methodologies for reduction, deoxygenation, and the direct use of oxygenated bonds to access functionalized and useful organic molecules. In organic
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through the manufacturing and characterization of test devices. To face the multiple challenges of the energy transition, whether at the generation, transport or use of electricity, extremely efficient
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associated with pesticide use, together with the increasing incidence of resistance cases in pests and pathogens, have prompted global policy initiatives to reduce pesticide dependence. Integrating robotic