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, learning, visual literacy, collaboration, shared spaces, physical installations, user experiments. All details here: https://bivwac.fr/jobs/ Where to apply E-mail phd-26_bivwac@inria.fr Requirements Research
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stability analysis and control, machine learning, dimensionality reduction and high-performance computing. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UPR3346-NADMAA-159/Default.aspx
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 17 days ago
Website https://jobs.inria.fr/public/classic/en/offres/2026-09928 Requirements Skills/Qualifications Profile: - The candidate is completing a Master's or engineering’s degree in Computer Vision, Electrical
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differential equation models of bacterial persistence. A particular challenge, both for simulation and for machine learning, lies in the high dimensionality of these equations, which causes grid-based numerical
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plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
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, machine learning techniques, etc.) is desirable. This thesis offer within the AstroParticle and Cosmology Laboratory (APC) is part of the Deep Underground Neutrino Experiment (DUNE). DUNE is an
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | about 1 month ago
leverage machine learning techniques to bypass IO bottlenecks in the context of physics simulation on high-performance computing (HPC) clusters. This work is thus placed in a broader ``Machine Learning for
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algorithms for asthma. The methods to be employed will include cell culture, transcriptomics, proteomics, multiplex assays, flow cytometry, and machine learning. This project combines expertise in cell biology
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 17 days ago
Computer Science, Machine Learning, Bioinformatics, Computational Biology, or related fields. Strong experience in deep learning, ideally with PyTorch. Proven experience with graph neural networks, geometric deep
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plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will