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IMT Atlantique, ULCO and Université de Rouen. In particular, the design of the models will be guided by results of the analysis of Wikipedia data obtained by other teams, and discussions with a
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simulation. A significant part of the work will involve scientific programming, development of numerical methods, and in-depth physical analysis of flow phenomena. The ideal candidate is passionate about new
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artificial intelligence techniques in the context of physical modelling. Proficiency in signal and data processing, including time- and frequency-domain analysis. Familiarity with instrumentation for non
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(synchrotron-based), fluid inclusion analysis (FIA), Raman spectroscopy, ICP-MS, and isotope geochemistry. The results of this work, combined with those of parallel studies carried out within the GEOTHERBAMINE
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learning applied to dynamic systems; Proficiency in key machine learning libraries (PyTorch, JAX, etc.); Mastery of Python and the software ecosystem for scientific data analysis and management (NumPy
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datasets for the broader research community. The student will receive comprehensive training in advanced experimental techniques, data analysis, and multiphase flow physics, preparing them for careers in
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analysis. Previous experience in conducting plant experiments is highly recommended. The candidate should demonstrate strong motivation and a clear interest in plant physiology as well as in the study of
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Statistical Sciences), where the doctoral candidate will gain advanced statistical expertise in target trial emulation and real-world data analysis. The second secondment will be in UMC Utrecht (Department
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 3 months ago
. Forbes, S. Borkowski, S. Heidmann, and L. Meyer. Massive analysis of multidimensional astrophysical data by inverse regression of physical models. In GRETSI 2023 - XXIXème Colloque Francophone de
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comprehensive platform for data extraction, analysis, and version control, providing access to highly curated datasets in a machine learning-friendly format. This PhD is part of the CARES project (Chemically