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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | about 2 hours ago
scientists developing computational simulations in various fields will be encouraged, in particular with the Gysela code, which is part of the ITER project. The project will first focus on dimensionality
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). At the LGRE, an innovative pilot-scale setup was previously developed to study magnesium combustion. This setup consists of a turbulent burner and a combustion chamber in which the flame is confined. Procedures
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of thermal degradation. This research has enabled the development of effective kinetic models, based on parallel or sequential reactions. However, the actual operating conditions of pellet stoves
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apply together to develop the following research project : Development of a reverse-engineering methodology to formulate concretes in case of extended lifetime infrastructure This postdoctoral position is
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strategy Transport subscription up to 75 % supported Sustainable mobility package, in line with the institution’s commitment to sustainable development Access to university restaurants with a preferential
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des systèmes actuels existants. As part of the development of smart and/or functional textiles, we propose a research topic combining several areas of expertise available within the laboratory
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or roving (parallelization, regularity), (ii) development of optimized wrapped yarns and study of the influence of wrapping parameters, (iii) yarn impregnation and composite manufacturing using various
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process using inherent network constraints. The scientific objective is to develop a theoretical and experimental framework addressing resource allocation problems in a distributed environment [4][5]. Four
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techniques in continuous measurement spaces that have undergone significant fundamental and algorithmic developments over the past 15 years. In particular, they make it possible to explicitly formulate
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, distributed intelligence. Techniques such as Federated Learning (FL), Swarm Learning (SL), and Transfer Learning enable organizations to jointly train models without sharing raw sensitive data. However, these