341 structures "https:" "https:" "https:" "https:" "https:" "https:" "Helmholtz Zentrum Geesthacht" positions at CNRS
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), Ryoji Shinya (Meiji University, Japan). Background: Mignerot et al. 2024 https://doi.org/10.7554/eLife.88253.2 Kanzaki et al. 2021 https://doi.org/10.1038/s41598-021-95863-1 Our team (http://ibv.unice.fr
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photophysics under conditions simulating those of the interstellar medium. Structural characterization, electronic absorption, electronic fluorescence, and recurrent fluorescence are studied through in situ
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internationally recognized for its leadership in state-of-the-art theoretical methods in nuclear structure and reactions, nuclear astrophysics and dynamics. Our group specializes in many-body theories (from nuclear
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(pore sizes below 100 nm) structures. Excellent scintillation properties, particularly a high radioluminescence light yield. Maximal transparency to ensure that luminescence flashes can be detected deep
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implement machine learning models dedicated to the prediction, interpretation, and quantitative analysis of Raman vibrational spectra, establishing explicit links between structure, local chemical environment
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output. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR137-HENJAF-017/Default.aspx Requirements Research FieldPhysicsEducation LevelMaster Degree or equivalent Research
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the conditions of their formation as well as their structure and stability. The transformation or formation of extra species during oxidative water treatment processes will be studied varying different parameters
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earthquake in 2023, Ml > 5). These events, often associated with the reactivation of inherited faults, leave traces in geological and archaeological structures that are sometimes difficult to identify and date
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the framework of the PEPR Sous-Sol project ORGMET conducted by a consortium of four French laboratories GET, INEEL/ESRF, LFCR and IPREM (https://www.soussol-bien-commun.fr/fr/appel-projets-2024/orgmet
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collaborators The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning-for-integrative - genomics/) at Institut Pasteur, led by Laura Cantini, works at