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of the contact line, which is still only partially understood and predicted. The present thesis proposes to develop an original experimental approach based on the simultaneous coupling of several optical
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the analysis of aircraft-based observations, controlled laboratory experiments, and the development of advanced simulation tools. In parallel, GRAIL addresses key technological challenges, including
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water sensor at the molecular level. Our measurement techniques and numerical models based on constrained regularization algorithms allow us to link these measurements with other techniques including
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means contributing to a better tomorrow. Whether you are a current resident of our New Haven-based community- eligible for opportunities through the New Haven Hiring Initiative or a newcomer, interested
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-driven methods provide excellent performance under low or cyclo-stationary regimes but struggle with highly dynamic and rapidly varying conditions; conversely, model-based state observers ensure robustness
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Débarre at the Lab. for Interdisciplinary Physics in Grenoble and of Dr Sigolène Lecuyer at the Physics Laboratory at ENS Lyon. You will be based principally in Grenoble, with secondments in Lyon and other
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, optimizing, and deploying AI models on HPC and GPU-based systems. Provide guidance on performance optimization, scaling, and efficient resource utilization. Contribute to architectural and design decisions in
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Machine Learning (ML) models, including automated testing, reproducible builds, controlled release strategies, and governance workflow integration. Build and manage containerized infrastructure on AKS
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20 Mar 2026 Job Information Organisation/Company Warsaw University of Technology Research Field Computer science » 3 D modelling Computer science » Autonomic computing Computer science » Computer
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | about 2 months ago
-scale (~10’s of km2) permafrost thermo-hydrological hybrid twin, to be coupled with state-of-the-art freezing/thawing soil mechanics machine learning-based surrogate models (Richa et al., 2024, Tristani