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assessment under application-relevant conditions for separation technologies, this project aims to accelerate the identification of novel materials for CO2 capture Specifically, this PhD project combines
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mutual agreement. We develop computational methods to accelerate materials discovery through defect engineering, with a focus on extreme environments. Application areas include fusion reactors, hydrogen
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the data. Job description This PhD project focuses on developing learning-based strategies for accelerated data acquisition, physics-informed image reconstruction, and quantitative inference
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conclude on December 31st 2029. The goal of this research effort is to apply machine learning (ML) techniques, in particular (equivariant) graph neural networks to accelerate the creation of all physical