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remain poorly understood. Their structural heterogeneity and chemical complexity make accurate atomistic modeling particularly challenging. Recent advances in machine learning approaches provide a powerful
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machine learning for cybersecurity, current systems remain largely based on pattern recognition and struggle to incorporate contextual reasoning, temporal dependencies, and relationships between entities
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. The successful applicant will develop a predictive pipeline using atomistic modeling and machine learning to identify optimal "seeds" for directing crystal growth, followed by rigorous experimental testing
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they are mainly based on predetermined rules of behavior chosen by the designer. More recently, methods derived from machine learning provided impressive results. However most are datadriven, meaning
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 6 days ago
work algorithm design and their theoretical analysis numerical simulations writing scientific papers Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2026-09888 Requirements Skills
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, understanding and predicting their thermal conductivity from first principles calculations is very challenging. In this doctoral research project, we plan to use machine learning potentials to investigate
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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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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
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: Modern machine learning approaches are increasingly exploited to automate and optimize fault detection and classification. We propose to investigate methods that improve diagnostics under under-represented
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 12 days ago
joint project between the laboratory “Reproduction et Développement des Plants” at Ecole Normale Supérieure at Lyon (https://www.ens-lyon.fr/RDP/ ), Mosaic team at Inria Lyon (https://team.inria.fr/mosaic