828 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" uni jobs in France
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | about 2 months ago
appreciated : Interest in privacy, data protection, or evaluation of machine learning models. Experience with reproducible experiments (code organization, documentation, version control) is appreciated
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University, but there will be a close collaboration with the group of Léa-Laetitia Pontani (LJP). Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR7332-CATLEV-053/Candidater.aspx Requirements
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
-PULSE addresses a key open question in responsible AI: can we design practical machine learning systems that satisfy strong privacy guarantees [1] and fairness [2] constraints simultaneously, without
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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | about 2 months ago
Research Infrastructure? No Offer Description The Inria center of the University of Bordeaux is a public scientific institute located in Talence (France). The Auctus team (https://auctus
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French National Research Institute for Agriculture, Food, and the Environment (INRAE) | Versailles, le de France | France | about 2 months ago
the epigenetic marking of new DNA sequences, (ii) how these chromatin marks help instruct the silencing of the RNA transcribed by these sequences, and (iii) which actors regulate the propagation of the RNAi
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for managing smart cities. The team has gained substantial experience in machine learning for road traffic monitoring. They are now keen to thoroughly explore the additional opportunities presented by
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Chromatography Coupled with Mass Spectrometry (Nano-FAC-MS). Anal. Chim. Acta 2023, 1261, 341227. https://doi.org/10.1016/j.aca.2023.341227
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for Horticulture and Phenotyping) team research topics focus on low cost computer vision and machine learning, simulation assisted plant phenotyping and machine learning based data mining for plant biology
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been revolutionized in recent years by machine learned interatomic potentials (MLIP), and questions that were impossible to tackle five years ago can now be addressed. The state-of-the-art approach
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