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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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-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