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evaluation Knowledge of numerical simulation, for example with land surface or hydrological models Genuine interest in data science and earth sciences Good organizational skills and ability to work both
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to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we
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modeling and numerical methods Experience with multiphase flow modeling (e.g., TFM, CFD-DEM, DNS, LBM) Solid programming skills Experience working in Linux/HPC environments Ability to conduct independent
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Centre de Mise en Forme des Matériaux (CEMEF) | Sophia Antipolis, Provence Alpes Cote d Azur | France | 11 days ago
-doctorate (CEA LIST) will aim to develop numerical modeling of Non-Destructive Testing (NDT) on the virtual grain structures obtained. Context: In the nuclear industry, the integrity of welded components is
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of mechanical engineering. Principal activities · Development of numerical tools for simulation of flanging process, especially for high strength steel. The developed models aim to feasibility analysis using
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | 2 days ago
science as part of the ERC Starting Grant project Incorwave, which aims to develop advanced numerical and mathematical methods for passive seismic imaging. The research will focus on two key-directions: (1
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modeling strategies for multiphase flow systems. The PhD topic is on exploring how emerging quantum computing methods can be integrated with classical numerical models to improve the simulation of complex
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/H2 flames in porous burners using direct numerical simulations (DNS) to understand NOx formation mechanisms. The research, in collaboration with CEA, will utilize the CFD code CONVERGE, employing
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-making tool for smoldering fire protection, a reliable predictive numerical model is needed. Understanding and developing a multiphysics model to predict the mechanisms governing smoldering fire in the
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machine learning algorithms for the prediction of manufacturing processes in composite materials. Development of user subroutines for finite element constitutive models Validation of model and numerical