129 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" positions in France
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foundations in classical probability theory and can be seen as a generalization of the Bayesian framework, bringing an additional degree of flexibility to express different types of uncertainty. In machine
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within the ANITI HUCAD project which aims to develop Artificial Intelligence (AI) systems fostering human-machine collaboration for effective deliberations. It aims to enhance the argumentation
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 3 months ago
the Statify team, located in Inria Montbonnot. He or she will have access to a team of experts in high-performance computing and machine learning that will help him or her to kickstart the project under
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) - Implement a demonstrator of the platform where a human learns how to operate a CNC machine with the help of a social robot, through the digital twins and interfaces. This includes: o Defining a learning
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research projects within LIA’s CORNET team, focusing on: Network and cloud systems, Virtualized network systems, Cloud and edge computing technologies and machine learning related topics
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solid experience in programming, particularly in Python and JavaScript. Significant experience in data science and machine learning will be highly valued. You like to work in a team while demonstrating
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cause abnormal or unsafe behavior. (2) Evaluate their effects on performance, safety, and security metrics. (3) Propose and validate mitigation and hardening techniques at the model, system, and learning
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 3 months ago
to apply Website https://jobs.inria.fr/public/classic/en/offres/2025-09273 Requirements Skills/Qualifications We are seeking highly motivated candidates with a background in machine learning and medical
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machine learning to model network behavior from real-world measurements (e.g., [7]). Although promising, these approaches still face three major limitations: (i) they often rely on idealized and extensive
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surfaces. Consequently, it is essential to develop mobile measurement instruments and acquire comprehensive datasets to validate and enhance the models. This PhD thesis project, a collaboration between COLAS