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Grenoble Observatory of Universe Sciences (OSUG) and a joint research unit (CNRS, UGA) with approximately 170 staff members and numerous temporary employees. https://ipag.osug.fr/ The CHARM team, led by
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, machine learning (PINN, supervised learning) - Python/PyTorch programming - Autonomy, curiosity, and adaptability - Excellent writing skills Specific Requirements The doctoral student's host laboratory is
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of command-line tools, Python, or R for data analysis is a significant advantage. Language: Proficiency in English (written and spoken) is mandatory, as this is an international collaboration project. French
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 14 hours ago
component to conceive novel setups and collect data to compare with simulations. Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2025-09688 Requirements Skills/Qualifications
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or equivalent) in physics A background in solid state physics is highly recommended Aptitudes in programming (Python) are a plus Proficiency in English (working language at the ESRF) Working conditions Your shall
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alignment, lasers, fibre optics, or low-flux detection is a strong asset. • Skills in modelling and scientific computing (Python, Matlab, etc.). • A rigorous, hands-on approach and enthusiasm for working
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access to an exceptional international research environment offering unique clinical facilities. Requirements: Master's in computer science or equivalent Python/C++ programming skills Strong knowledge
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Website https://emploi.cnrs.fr/Candidat/Offre/UPR2357-PATACH-008/Candidater.aspx Requirements Research FieldBiological sciencesEducation LevelPhD or equivalent Research FieldBiological sciencesEducation
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geophysics, physics, applied mathematics, or computer science will be considered. Expertise in high-level programming language like MATLAB or Python is essential. Where to apply Website https://emploi.cnrs.fr
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experience in software like Julia and Python is desirable. 1.7 References 1. Nérot, B., Lamaison, N., Mabrouk, M.T., Bavière, R., Lacarrière, B., 2023, Optimization framework for evaluating urban thermal