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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
Text-to-Video Generation Models Supervision : Dr Stéphane Lathuilière (INRIA-UGA) Funding : BPI contract Contexte : Recent advancements in generative AI, and in particular diffusion models [1,2], have
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) to soft matter or related systems; basic knowledge of scattering data modelling is essential. Data analysis skills and programming experience in Python or MATLAB. Ability to work effectively with multi
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expression analysis, molecular and cell biology, confocal and two photon imaging, in vivo gene delivery amongst other techniques, and aptitude in working animal models. Where to apply Website https
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the equipment, including the annual budget and the personal computer provided by the MARBEC laboratory for modelling and mapping work. - main mission: The researcher’s main role will be to model and integrate
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Potentials (MLIP), machine learning (ML) predictive models and AI tools. Activities : Computer science implying ML and AI tools applied to material science Where to apply Website https://umontpellier.nous
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(pure and metal-doped) and carbon chains in supernova ejecta. • Apply molecular dynamics (MD) methods to simulate the formation of alumina agglomerates. • Use ab initio wave function methods to model
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the CARNOT HyLBM project, which aims to develop advanced numerical models for hydrogen storage in nanostructured metal hydride reactors. The researcher will contribute to the development of advanced
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postdoctoral researcher to join our group at the Institute of Genetics and Development in Rennes (IGDR), France (https://igdr.univ-rennes.fr/en ). Research in the Cyclochondria team led by Giulia Bertolin (https
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focused on a poorly characterized family of Cryptosporidium proteins ; • the opportunity to establish and apply functional genomic approaches in an emerging parasite model ; • substantial involvement in
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generation, targeted atomistic simulations, structural descriptor extraction, and predictive models. The work will aim to establish links between local pore geometry, structural disorder, sodium adsorption