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seeking a highly motivated doctoral student to study physics- and thermodynamics-informed machine learning (ML) for the mechanics of material failure. The project focuses on learning the effective behavior
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The Data-Driven Mechanics Laboratory is seeking a highly motivated doctoral student to study physics- and thermodynamics-informed machine learning (ML) for the mechanics of material failure
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on either component. This interdisciplinary project bridges the gap between granular physics and materials science. Main roles and responsibilities: Research in this project involves a synergistic combination
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may be placed on either component. This interdisciplinary project bridges the gap between granular physics and materials science.Main roles and responsibilities:Research in this project involves a
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of beyond Cold and Collisionless Dark Matter simulations. The position will be to leverage physics-informed neural networks to accelerate simulations of Fuzzy/Axion Dark Matter. However there will be
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engineering or physics and have experience in experimental fluid dynamics and wind energy. It is highly desirable that the candidates have experience in the use and analysis of data from scanning Doppler wind
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corresponding selection process Coordination of reporting (scientific and administrative) Organisation of events and meetings of different comittees, contribution to consortium communication Participation
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-level academic education and a proven track record in conducting and publishing outstanding research in the field of physical metallurgy. Skills of direct interest include a strong command of the
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’ developments and key results Your profile : PhD in photonics, semiconductor physics, electrical engineering or closely related field with high academic record indicated by publications in peer-reviewed
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and collaborate with the Department of Process Engineering and the Department of Water Resources and Drinking Water. EPFL professors are committed to excellence in undergraduate and graduate level