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: - reading and understanding theoretical physics articles, in particular with a view to deriving equivalent computer algorithms - develop and/or adapt a physical modem and/or a Monte Carlo type code in
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Machine Learning for engineering. Project description Supervisor: Lucas LESTANDI Keywords: Physics Informed ML, ML for engineering, surrogate modeling, reduced order modelling Topic open: Background
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at Saclay (20 km South of Paris). ABOUT YOU ! You are graduated from a PhD ? You are specialized in Energy Economics ? You have at least minimum 1 to 2 years of experience in setting up and analysing
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the creative and innovative potential of researchers holding a PhD and who wish to acquire new skills through advanced training, international, interdisciplinary and inter-sectoral mobility. MSCA Postdoctoral
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, particularly numerical fluid mechanics, reduced-order modelling, or machine learning. You are passionate about performing original research at the intersection of these different subjects, and interested in
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, Mechanics of Materials, Elasto-Plasticity Topic open: Model-free data-driven approaches offer an interesting alternative to classical constitutive model-based approaches, and to machine-learning approaches
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FieldPhysicsYears of Research ExperienceNone Research FieldTechnologyYears of Research ExperienceNone Additional Information Eligibility criteria - The candidate must have a PhD in the field of organic chemistry for
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of exapted TE genes. To be eligible, the candidate must earn a PhD with less than 8 year of research experience in European labs, and has not spent more than 12 months in France over the last three years
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the top 300 globally for Engineering, and in the top 500 for Physical and Computer Sciences in the Times Higher Education World University Rankings by Subject 2024, highlighting its multidisciplinary
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this area. Additionally, Centrale Nantes is positioned in the top 300 globally for Engineering, and in the top 500 for Physical and Computer Sciences in the Times Higher Education World University Rankings