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coupling, damage evolution, and anisotropic material behaviour. These developments will result in a virtual digital framework capable of providing predictive indicators, virtual testing, and decision-support
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capable of automatically selecting the simulation strategy best suited to a given prediction objective, while optimizing the trade-off between accuracy and computational cost. The work will build on multi
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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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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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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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damage mechanisms governing shell failure. The results will lead to the development of predictive design rules and digital tools to support high-throughput investment casting strategies. In the longer term
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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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coupled ice-sheet–ocean interactions within ESMs, high resolutions required to resolve ice-shelf cavities make this approach relatively expensive, given the large number of ensemble members needed
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for the development of more predictive preclinical platforms. This PhD project is part of the ANR JCJC EXOFLEX project, which aims to develop an instrumented microfluidic platform capable of simultaneously controlling
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parameters (periodicity, thickness, refractive indices) and material properties to maximize the ultrasonic sensitivity of the sensor. 3. Experimental Validation: Comparison of the predicted simulation