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PhD position: Nanoengineering refractory compositionally complex alloys for extreme conditions (M/F)
of the materials. Complementary structural and micromechanical characterizations will be carried out after irradiation to analyze the evolution of microstructure and properties. The objective is to establish
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batteries have enabled the development of portable electronics and electric vehicles, due to their great gravimetric and volumetric energy densities. The tremendous success of these systems and the
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generalized parton distributions (GPDs). A key component of the PhD will also involve the development of novel algorithms designed to overcome current computational and theoretical challenges in hadron
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between contaminants, • the potential role of PFAS and antibiotics in the selection of antibiotic resistance genes (ARGs). The successful candidate will contribute to the development of an integrated urban
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) and a global ocean model (NEMO). The selected candidate will contribute to the ANR-AIAI project (https://anr-aiai.github.io ). Scientific Context The melting of Antarctic ice shelves by the ocean is a
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of this project is to enhance the efficiency, sustainability, and environmental compatibility of CO2 reduction to methanol through the development of advanced catalytic materials chosen among intermetallic
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/ implement the experimental devices necessary for its scientific activity, as well as the design, development and use of instruments. . The researcher will work within the ILE group (4 faculty researchers, 3
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; https://cordis.europa.eu/project/id/101087562 ), the postdoctoral researcher will be responsible for characterizing metal-rich meteorites, particularly primitive achondrites, using scanning electron
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. However, these development typically requires very large training datasets generated from computationally expensive calculations, which represents a major bottleneck for the study of complex catalytic
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computing. This evolution enables services to be deployed across the edge-network-cloud continuum [1], leveraging heterogeneous resources from edge devices (e.g., smartphones, microcontrollers) to cloud data