132 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" scholarships in France
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congested architectures. For more information, visit my professional website: https://iscr.univ-rennes.fr/daniel-muller Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR6226-DANMUL-005
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that they can integrate it into their large-scale quantum computer system engineering models. SKILLS. Candidates must have a high-quality background in quantum information or quantum physics, and an
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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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part of this project, the thesis will focus, on the one hand, on a detailed analysis of gas phase inhibition kinetics by combining experimental and numerical studies to determine global parameters (auto
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new thermoelectric materials using data science and machine learning methods applied to materials, based on expert-reviewed experimental data from the literature and public databases (notably
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, deformation mechanisms and mechanical performance, ultimately enabling more efficient design and optimization of advanced structural materials. [1] https://www.pepr-diadem.fr/projet/ammetis-2/ [2] https
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forces on each mode in order to reduce (i.e., cool) their individual vibrations. The student will be closely guided by the advisors and will acquire both theoretical and experimental skills
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Jupiter's polar regions using computer simulations. The core of the project consists of coupling a photochemical model (developed and used in numerous planetary applications) with an electron transport model
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after thermal treatment to maintain gas flow and optimal detection. The PhD student will learn the synthesis of nanomaterials and their integration with 3D printing techniques. The synthesized materials
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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