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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 4 days ago
and Objective: Tabular and Time-series foundational models have become very popular due to their high accuracy relying solely on In-Context Learning (ICL) [3, 4]. However, one still requires training
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, as well as with industry and society. The faculty entertains various successful and growing teaching programs and supports lifelong learning activities. Your role As a professor in the Department
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contact Enrico Glaab : Your profile We seek a bioinformatician or computational biologist who is well versed in the machine learning and statistical analysis of biomedical data, the use of artificial
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learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability landscape (IPLS) from limited experimental
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Inria, the French national research institute for the digital sciences | Grenoble, Rhone Alpes | France | 4 days ago
the Thoth project team (https://thoth.inrialpes.fr/ ) within the Inria Centre at Université Grenoble Alpes (https://www.inria.fr/en/inria-center-universite-grenoble-alpes ) to work under the supervision
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out a rigorous scientific study aimed at comparing the performance of deep learning models in detecting complex visual anomalies. Take charge of the entire study, define the evaluation criteria and
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LPSM (Laboratoire de probabilité et modèles aléatoires) in Paris. Main mission : The project lies at the interface between quantitative ecology and statistical learning. It brings together the expertise
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develop and implement machine learning approaches to analyze these data and extract relevant indicators to improve water resources management. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant
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Context Recent advances in computer vision and generative AI have enabled major breakthroughs in image and video understanding. However, modern deep learning models remain critically dependent
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in privacy preserving machine learning (ML) within the SSF-ML-DH project, under the supervision of Olivier Cappé (CNRS, DI ENS) and Jamal Atif (Ecole Polytechnique, CMAP). Funding is available for two