4 combinators-"https:"-"CIPMM---Systemic-Neurophysiology" Fellowship positions at Université Gustave Eiffel
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samples will be available. 2 - Objectives To address these fundamental limitations, this project aims to build trust in physical predictions of long-term degradation through an original combination
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addresses the need for data-driven and hybrid modeling approaches that combine physics-based knowledge with artificial intelligence (AI) algorithms for accurate, interpretable, and robust health state
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of the grain assembly (e.g. frictional bridging, distributed damage). • (O3) Link microscale dissipation to macroscopic Gc by combining micro-tests, in situ tomography, and modeling. • (O4) Improve tensile
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to identify, quantify and compare these precursors using controlled laboratory experiments on granular systems combined with advanced optical measurements, with the objective of improving failure predictability