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at Université Paris Cité, 75013 Paris. The LIED aims to develop both fundamental and applied research addressing the challenges of energy and climate transitions. In this context, the laboratory promotes a
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PhD position: Nanoengineering refractory compositionally complex alloys for extreme conditions (M/F)
the University of Arizona (U of A), within the framework of a CNRS–U of A doctoral program supported by the International Research Center (IRC) for Global Grand Challenges. This PhD aims to develop and investigate
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synthesis due to steric hindrance and associated selectivity issues. Thesis Objective: This thesis aims to develop new synthetic methodologies based on controlled cationic chemistry to access these highly
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part of the research theme 'Planets and Moons', and will be integrated within the ERC - IceFloods (https://lpg-umr6112.fr/en/erc-icefloods/ ). This thesis will aim to characterize the contribution of ice
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researcher, and a research engineer. Field experiments will take place in Seville for Cataglyphis velox and in Australia for Myrmecia species. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR5169
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Stehly. Anne Paul, PI of the MACIV project, will assist with the supervision. These researchers, and more broadly the Waves and Structure team, have been heavily involved in the development of methods
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organised between the APC team and the University of Chicago team, as well as face-to-face meetings in Paris and CERN, and annual visits to Chicago A strong interest in computer science (software development
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with Novel upscaling assisted by artificial intelligence) project financed by PEPR DIADEM (a public funding program on the development of innovative materials using artificial intelligence). The relevant
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). The project is funded by the French National Research Agency (ANR) through the ePulse2 project (https://anr.fr/Projet-ANR-25-CE08-0397 ), which focuses on two aeronautical materials exhibiting carbides and/or
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many genetic mutations "uninterpretable" in clinical settings. Our project will develop an artificial intelligence approach capable of predicting, for each mutation and each cell type, its impact on