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engineering the materials and processes in a multiscale approach (local flow and diffusion conditions during ad- and desorption, advanced desorption strategies and process scheduling). Your tasks The project
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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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25 May 2024 Job Information Organisation/Company Empa Research Field Computer science » Other Engineering » Systems engineering Engineering » Other Mathematics » Applied mathematics Physics
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and Processes for Power-to-X and the Advanced Colloidal Materials Engineering group search for a Postdoctoral researcher in the field of materials and process development for direct air capture of CO2
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Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living
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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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, with possible starting dates after July 2024. Your profile holds a Master’s degree preferably in physics, alternatively in material sciences has a background in condensed matter physics ideally has
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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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recent PhD degree, preferably in physics, chemical engineering, materials science, or mechanical engineering. Knowledge of the theory and demonstrated proficiency with QC modeling is mandatory
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profile The applicants must hold a recent PhD degree, preferably in physics, chemical engineering, materials science, or mechanical engineering. Knowledge of the theory and demonstrated proficiency with QC