119 machine-learning "https:" "https:" "https:" "https:" "https:" positions in France
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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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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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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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the detector’s lines of response. The candidate will develop a hardware attenuation correction by generating attenuation maps from 3D models of RF coils created using computer-aided design (CAD), or from clinical
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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
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, decision-making and control using data, have been proposed. For control or management applications, reinforcement learning (RL/DRL), a branch of machine learning, is a promising solution that involves
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and image processing. Prior experience with data fusion, machine learning, or super-resolution methods will be considered an asset. Candidates should be motivated to conduct independent scientific
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. 5, no. 2, pp. 354–379, 2012. [2] C. K. Williams and C. E. Rasmussen, Gaussian processes for machine learning. MIT press Cambridge, MA, 2006, vol. 2, no. 3. [3] G. Daras, H. Chung, C.-H. Lai, Y