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of surface sites makes theoretical understanding difficult. This project will develop and benchmark machine learning models to predict local electronic density of states (DOS) at alloy catalytic sites
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This position offers a unique opportunity to work at the intersection of statistical machine learning, control theory, and transport safety, in collaboration with researchers at Chalmers and the
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methods for creating innovative heat-exchanger geometries with high efficiency and sustainability in mind. You will use open-source machine learning software provided by Meta AI and OpenAI, deployed
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– from materials design and processing to machining, mainly of metals. Our expertise spans powder metallurgy, electroplating, additive manufacturing, and material removal, combined with advanced
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systems. This PhD project, part of a national initiative, aims to use AI to design and optimize thermal interface materials (TIMs). It combines machine learning, materials informatics, and experiments
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). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction solvers. Computational aeroacoustics. Swedish is not required