68 structures-"https:" "https:" "https:" "https:" "https:" "University of California, Santa Cruz" PhD positions at CNRS
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material for an intended structural application. To conduct this study, a nickel-based superalloy (Inconel 718) will be used as a model material because of its broad industrial applicability and its
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of both the filament and the substrate, pressure, gas phase precursor composition) with the film chemical structure and the resulting optical properties will be carried out. The DC candidate will be trained
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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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) - Signal processing and image analysis (Python) - Oral presentation of scientific results at meetings and international conferences. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR5295
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together expertise in physics, chemistry, nanoscience, and materials engineering. For more information about IS2M, please feel free to visit the website: https://www.is2m.uha.fr/ . The PhD candidate (M/F
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at the Center for Theoretical Physics in Marseille, France. Wikipedia is the classical example of a successful, large-scale collaborative project. Its production, structure and functioning have emerged through
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The expert will participate in the necessary methodological developments and analyses of airborne data recorded by the IAGOS research infrastructure (https://www.iagos.org ) and from other networks, to provide
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. Argument(ation) mining, the new and rapidly growing area of Natural Language Processing (NLP) and computational models of argument, aims at the automatic recognition of argument structures in large resources
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ultrasound, Laboratoire d'imagerie Biomedical, LIB , https://www.lib.upmc.fr/ ) and nanoparticle engineering ( PHENIX Laboratory https://phenix.cnrs.fr/ ). The LIB is located in the Centre de Recherche des
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, L. Estel, Analysis of thermal runaway events in French chemical industry, Journal of Loss Prevention in the Process Industries, 62 (2019) 103938. https://doi.org/10.1016/j.jlp.2019.103938 2. Y. Wang