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the mechanisms of sorption and diffusion; (iv) to establish relationships between molecular structure and adsorption properties; and finally (v) to combine experiments and simulations to predict the performance
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macro- scales at IJL, and to train machine learning models to predict the microstructure evolution at larger scales and longer times at SIMAP lab and Laboratoire Analyse et Modélisation pour la Biologie
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and with the 2AT team at Institut Pprime to develop an innovative jet-noise prediction tool. The researcher will develop a novel jet-noise prediction tool based on a resolvent analysis of the Navier
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al. 2019] and point-force Lagrangian models, with advanced post-processings [Vegad2024]. This work will be carried out with the YALES2 high-performance platform. Where to apply Website https
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modeling, photovoltaics, high-temperature experimentation, and solar energy technologies. Thermophotovoltaic (TPV) systems convert thermal radiation emitted by a hot surface into electricity using lowbandgap
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speech processing, a research question that is rapidly gaining in importance. The project is centred on the hypothesis that the cerebellum conveys predictions about upcoming speech sounds to the neocortex
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AI researchers from ANITI, IMT and CERFACS, as well as with researchers/engineers in weather forecastings from the CNRM (Météo-France). Hybridization methods between neural networks and physical models
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
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Starrydata2). The work will include the implementation of machine learning models (neural networks, random forests, SISSO), generative approaches for predicting crystal structures, the use of machine learning
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prediction models, and visualizing immense volumes of various types of data, generated by agri-robots and IoT devices. The most popular classes of autonomous agricultural devices include: weeding robots