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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | about 1 month ago
Website https://jobs.inria.fr/public/classic/en/offres/2026-09839 Requirements Skills/Qualifications Strong mathematical background. Knowledge in numerical optimization is a plus. Good programming skills in
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uptake, stress tolerance, and species interactions. A case in point is optimal partitioning theory (OPT), a dominant paradigm of plant resource allocation that describes the ability of plants to adjust
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environments. You have knowledge and skills in profiling, optimization, and code acceleration (parallelization, GPU porting) You know how to work independently and with the project management methodology You are
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: The ideal candidate should have: * Knowledge of machine learning, especially neural networks or graph neural network or federated learning. * Strong mathematical and algorithmic background (optimization
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distributed data processing closer to IoT devices. However, optimizing service placement in Fog environments remains a challenge due to the variability of Fog node resources, latency and energy constraints, and
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, regulatory, and traceability requirements. The aging of facilities, the gradual loss of tacit knowledge, and the increasing volume and heterogeneity of data make it difficult to optimize and sustainably secure
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schedule that was designed for maximal contention. As a result, systems optimized for worst-case scenarios can exhibit degraded performance under typical operating conditions. This observation points to a
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 10 days ago
, energy optimization, and sustainable urban mobility. The position focuses on the development of a hybrid electromobility model within the eMob-Twin platform, combining large-scale mobility data from
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. The core objective is to bridge model-based and learning-based approaches by embedding biomechanical knowledge into data-driven models to achieve robust, physically consistent, and context-aware control
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of the School, the Institut Mines-Télécom and the Institut Polytechnique de Paris Job requirements We are looking for a candidate with in-depth theoretical and practical knowledge in the field of optimization