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of Research Experience1 - 4 Additional Information Eligibility criteria We are looking for a doctor in particle physics with less than two years of experience after the PhD. Experience in machine learning and
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, ideally molecular dynamics and/or DFT. Scientific programming skills, particularly in Python, are expected. Familiarity with machine learning or generative AI methods applied to materials would be a strong
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. Development and integration of state-of-the-art machine learning techniques in the analysis and event reconstruction will be a major component of this work. - Characterization of silicon detection modules using
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to contribute to cutting-edge research in flat optics, with applications in high-resolution imaging, 3D depth perception, and compact optical devices. Key objectives: 1. Design and Simulate Achromatic
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astrophysics, cosmology, or a related field completed by the start date; strong programming skills; working knowledge of machine learning applied to astrophysics and cosmology, in particular simulation-based
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lie at the crossroads of multiple disciplines and involve expertise in optics, electronics, image and data processing (including machine learning), photophysics, chemistry and biology. The position is
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. This project aims to evaluate its potential as a regulator of the antitumor immune response. The site is accessible by tram (Line A, Campus Illkirch station) from the Strasbourg train station or by car (parking
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hold a PhD in computer science or mathematics, with computer algebra as a specialty. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7161-GOVVAN-019/Default.aspx Work Location(s
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stakeholders. The work will be carried out at the Cefe in Montpellier (France; https://www.cefe.cnrs.fr ), in close collaboration with the IMB in Bordeaux (France; https://www.math.u-bordeaux.fr/ ; regular
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with other partners involved in the MAESTRO/ORCESTRA field campaign and the MAESTRO team. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR8539-ISARIC-129/Default.aspx Requirements Research