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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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– 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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able to understand better how and why toxic oligomers form. We are also interested in using our technologies to study enzymes, which are nature's catalytic machines. Enzymes are very important for
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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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stereolithographic, 3D printing and textile techniques like tufting, machine-based embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and characterization technologies (X-ray micro
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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
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stereolithographic, 3D printing and textile techniques like tufting, machine-based embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and characterization technologies (X-ray micro