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approaches for machine learning. Research interests span representation learning, statistical inference, trustworthy machine learning and generative models. DATA is hosted at the interdisciplinary Centre de
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, machine learning or neuronal population analyses would be an advantage. Specific Requirements We are looking for a candidate with a strong interest in scientific software development and quantitative
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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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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 2 months ago
vision or video understanding; Multimodal learning; Large language models. Programming experience in Python and familiarity with deep learning frameworks such as PyTorch are expected. Knowledge of sign
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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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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 2 months ago
machine learning, explainable machine learning, fairness and data protection legislation. Privacy-preserving machine learning aims at learning (and publishing or applying) a model from data while the data
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | about 1 month ago
learning, privacy, security and distributed systems. Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2026-10411 Requirements Skills/Qualifications We are looking for a candidate with
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deep learning has led to the emergence of Transformers-type networks whose performance has revolutionized the field. These networks are the basis of next-generation image processing models (such as
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learning, particularly flow-matching generative models and protein language models. The research will focus on designing efficient generative models able to produce realistic conformational ensembles while
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on lncRNA structure. These experimental constraints will then be used to guide deep learning-assisted RNA 3D structure prediction tools, in order to generate ensembles of structural models. Clustering and