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), within the ATLAS group, in close collaboration with the French-Japanese ILANCE laboratory and the University of Tokyo. The successful candidate will become part of a large international collaboration and
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. Development and integration of state-of-the-art machine learning techniques in the analysis and event reconstruction will be a major component of this work. - Characterization of silicon detection modules using
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(focusing on polar and alpine environments) and geophysical fluid dynamics. We collaborate with several colleagues from the Physics Laboratory, who are experts in hydrodynamics/climate research (about 15 PIs
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properties of this protein, as well as its interactions with other cellular components. The obtained results will be compared with available experimental data, in collaboration with our partners. We
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. This activity is inherently multidisciplinary with strong collaborations with other scientific fields, as applied mathematics or statistical physics. Fluid mechanics is ubiquitous in geophysical and industrial
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information processing into an established consortium of experimental physicists and cancer biologists. The project establishes a close collaboration between IEMN and PhLAM, with the support of CANTHER
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-Brazilian collaboration in paleoclimatology, focusing on both modeling and the reconstruction of paleoclimates in tropical South America (e.g., joint oceanographic cruise AMARYLLIS-AMAGAS II, International
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experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in particular simulation-based inference), strong programming