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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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, and computational biology. The project includes collaborations with academic partners specialized in probabilistic modeling and experimental biophysics, notably through interactions with research groups
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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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to the development of energy models; • to access complementary experimental platforms; • to strengthen Franco-Canadian scientific collaborations. The project also benefits from industrial support through
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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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computational and energy costs of LLMs, this work will focus on smaller, domain-specific models. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR8254-SYLDES-026/Default.aspx Requirements
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production. By developing hybrid architectures combining ontologies, generative models, reinforcement learning, and uncertainty quantification, the PhD project addresses the challenges identified by ICCARE in
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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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. 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
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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