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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 11 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
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 5 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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, traceable industrial packaging that can be shared between manufacturers, we are recruiting a deep learning Post-doctoral researcher in our Mathematical and Electrical Engineering (MEE) Department. Using
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the knowledge acquired during the PhD with team members and acquire new knowledge. - Engage with the Local team at LIPN and the wider national community working on proof theory, programming languages and
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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quantitative and machine learning approaches ● Developing predictive models linking nuclear features to future cell fate ● Interacting with collaborators in imaging, computational biology, and developmental
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the research activities entrusted to the officer take place: This ANR project lies at the interface between statistical learning (mainly deep learning) and combinatorial optimization (mainly stochastic and
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 9 days ago
graph-based representations, system dynamics, and Physics-Informed Learning, as well as the implementation and validation of these models using real-world data. The developed models will be integrated
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Description Within the ANR HEBBIAN contract, the objective is to adapt bio-inspired Hebbian learning models recently proposed by one of the partners of this ANR (Frédéric Lavigne) in order to account for data
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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will