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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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mission of unlocking the “geometry of artificial intelligence” then please apply! What you will do Singular Learning Theory (SLT) is a mathematical framework for analysing statistical models that do not
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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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-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
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subsidiarity at the territorial scale," specifically through the "materials for energy storage" program. Using molecular modeling tools, the objective is to participate in the design of a single catalyst capable
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field measurements and laboratory experiments Comparing and evaluating existing numerical models, including XBeach and Watlab (developed at UCLouvain/iMMC) Developing and implementing improved numerical
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to use your mathematical skills to model and design optical systems for sustainable high-tech devices for billions of people? Do you like to develop and analyze numerical methods for partial differential
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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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decision is made Strong background in computational modeling and numerical methods Experience with multiphase flow modeling (e.g., TFM, CFD-DEM, DNS, LBM) Solid programming skills Experience working in Linux