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involved during capillary debinding through the use of specimens and model tests • Understand the influence of processing parameters on debinding mechanisms and kinetics • Determine the influence of
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Deep learning models, and in particular large language
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Structures team, which specializes in the observation and modeling of tectonic processes, from grain to lithospheric scale, and from seismic cycles to the growth of major geological structures. The research
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The postdoctoral fellow will participate in the PostGenAI@Paris AI Cluster (ANR) project at Sorbonne University, and more specifically in the "AI-Augmented Multiscale Modeling for Energy Storage" sub-project, whose
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on solubility, dissolution kinetics, and microbial influences. Your work will refine global carbon cycle models, enhance CO₂ sequestration understanding, and position you at the forefront of ocean
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CNRS/IN2P3 and Université Toulouse III - Paul Sabatier. The L2IT Particle Physics team contributes to understanding the dynamics of the scalar sector of the Standard Model through studies of the Higgs
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and process behavioral and electrophysiological data • Model behavior based on diffusion models and make explicit link with neurophysiological data • Conduct detailled statistical analysis • Write
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processes (Auger–Meitner decay, positron emission, etc.). -Identify relevant observables for attosecond pump–probe experiments, in collaboration with theoretical and experimental partners (Sorbonne Université
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organizing and conducting field sampling and measurement campaigns. They will be responsible for acquiring data, processing results, and promoting the knowledge gained. Their mission will be to characterize
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of the work is to advance and automate the use of ptychography on various sample types. In particular : 1) accelerate the reconstruction process using a multislice algorithm 2) allow a 3D reconstruction and 3