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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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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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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
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of these sensors. - Access to the laboratory's experimental characterization facilities. - A collaborative and multidisciplinary environment at the interface of theory, modeling, and experimentation. Scientific
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rules for ceramic shell mould clusters; Experimental validation of the relationships between microstructure and thermal shock resistance; Development of multi-scale finite element models for predicting
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to contextualise future observations of Jupiter without an external monitor of solar wind conditions (e.g. the JUICE probe), (iii) making more accurate predictions for exoplanetary radio emissions to aid in