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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | about 2 months ago
benefit from the hardware and software resources of the lab experimental platform. The student will be supervisedthroughoutthePhD by Margot Vulliez and David Daney, researchers specializing in modeling and
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the Artistic Director is unavailable. Champions innovation in producing models, institutional partnerships, and artistic operations. Develops long- and short-term programs to achieve the artistic goals
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". The activity will take place at the Centre Gestion Scientifique (CGS), Mines Paris-PSL, Université Paris Sciences et Lettres https://www.cgs.minesparis.psl.eu/ This is a tenure track position with a reduced
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, design and analysis of virus-derived RNA libraries, and development of machine learning models for detecting functional elements in viral metagenomic datasets. This project is a collaboration with the
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methods and GIS tools to create 3D environments that integrate high-resolution 3D urban models with climate-resilience objectives. This should help policymakers and citizens optimize energy-efficiency
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generative artificial intelligence, machine learning, and ontologies to automatically align heterogeneous competency frameworks. The research will focus on: Formal modelling of competencies and educational
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with familiarity with foundation models (e.g. vision language models) and the ability to design and prototype innovative, reliable and reproducible solutions for complex 3D scene understanding tasks
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of metamaterial-based supports in attenuating vibrations of the single beam/coupled beam/metabeam systems. • Development of the mathematical models of the single beam/coupled beam/metabeam systems under
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governance and transformational innovation in learning and teaching. The Vice-Principal will lead the development of future-focused academic and operating models that support a leading international
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microscopical information and are translatable to humans. You will specifically work on data integration and biophysical modelling to make the most of the wealth of information in the multi-modal datasets