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requirements for training data, memory, and computing power while maintaining a high level of accuracy. Particular attention will be paid to the explainability and embedding of the models to facilitate
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responsible for pressure generation and the physical factors limiting eversion. Finally, the experimental data will be integrated into a biophysical model linking pressure, geometry, and tissue mechanical
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, particularly EEG; • Programming experience, particularly in R and Python; • Proficiency in statistical methods commonly used in psychology and neuroscience (ANOVA, linear models, etc.); • Familiarity with
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model of magmatic oceans on forming planets based on laboratory experiments. At present, high-pressure molten silicates are modelled only in a rudimentary manner, due to a lack of data on activity
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) biometrics, with methodological developments in statistics, computer science, and mathematics for modeling living organisms; (ii) ecology and evolution, studied from the molecular and genomic levels
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. The PhD student will be in charge of analyzing the full data set in order to finalize the background model, study the two-neutrino double-beta decay of 82-Se, and search for neutrinoless modes. Where
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make it possible to obtain new constraints on the parameters of the standard cosmological model and to test possible extensions to it. Achieving these goals will require extremely precise control
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at the Laplace laboratory, where numerical and analytical models are being developed to predict plasma potential control and flux entrainment from polarized electrodes. During the third year of the thesis project
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-fidelity modeling approaches combining simulations of different fidelity levels, ranging from RANS calculations to high-fidelity LES, possibly associated with various levels of geometric or physical
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. This PhD project in Natural Language Processing aims to design generative models capable of automatically simplifying texts into Easy-to-Read and Understand language while preserving their discourse