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STLO) (https://eng-stlo.rennes.hub.inrae.fr/ ). This work is part of the “Predicting the browning of dairy powders by kinetic modeling of Maillard reaction and caramelization during drying and storage
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predict interaction effects. Unlike robot-specific neural network models, the proposed approach aims to learn a universal representation of local interactions (fluid-structure, robot-robot, robot-object
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velocity anomalies (Vp, Vp/Vs). These observations will be quantitatively compared with model predictions to test whether the system is dominated by the geometric interactions of the faults, the rheological
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, France to complete the project. Objectives The objective of this PhD project is to develop advanced numerical modelling tools to support the digital twins of refractory materials, with the aim
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tomography, high-pressure reactor) with numerical modelling (COMSOL) to quantify and predict dissolution. • Select and characterise biogenic shells (foraminifera, pteropods) from oceanographic cruises
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 2 months ago
. Preferred qualifications include experience with: Optimal Control and/or Model Predictive Control (MPC). Modeling of deformable parts. Real-time numerical optimization. LanguagesFRENCHLevelBasic
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of the MOST (Modelling and Simulation of Turbulence) team focus on the numerical prediction of turbulent and multiphase flows with a broad range of objectives from fundamental understanding of flow properties
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have access to state-of-the-art facilities, including cleanroom microfabrication, laser micromachining, 3D printing, advanced microscopy, and numerical modeling tools. The project benefits from a close
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interferometry, etc.), (v) Predictive modelling of coupled phenomena (reactive transport, rock-water interactions, etc.), (vi) Uncertainty quantification (Monte Carlo, meta-modelling), and (vii) Risk analysis. The
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predictive digital twin of the face to better understand, model, and rehabilitate facial expressions. The project is led by the BMBI laboratory (UTC-CNRS) and brings together a multidisciplinary consortium