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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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significantly to the dose delivered to biological tissues. Monte Carlo simulations, especially those based on the Geant4 toolkit, rely on hadronic models whose predictive accuracy is still limited by
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data) will help validate observations and refine predictive models. Automated monitoring tools (scripts, dashboards, alerts) incorporating machine learning algorithms or statistical methods will be
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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
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the mobilome) can reveal higher-order patterns and help building predictive models. Based on the possibility to infer functions and traits from metagenomes, early comparative analyses suggested that taxonomic
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the growth of carbon chains. • Collaborate with astrochemists and observers to validate theoretical predictions. This position will focus on modeling dust production in supernova explosions, a key process for