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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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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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carbon species once deemed too unstable for practical use, were tamed through the pioneering work of Bertrand and Arduengo, who synthesized the first stable singlet carbenes. In particular, N
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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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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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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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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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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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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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gas analysis, a GC/MS, and an HPLC/MS. DFT calculations will be performed using annual allocations on national high-performance computing centers. More details here: https://iramis.cea.fr/en/nimbe/lcmce