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in materials theory. The Division of materials theory offers world-class research in solid-state theory, using both numerical and analytical methods. The research is focused on magnetism, electronic
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-like electronic materials could be built from two of the most abundant elements on Earth? We are looking for a postdoctoral researcher to lead the experimental side of that effort. About the project We
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-performance computing, particularly in the theory of efficient string and data structures, sequence indexing, and large-scale data analysis. Experience with the Burrows–Wheeler transform, pattern matching, and
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candidate will join the Scientific Machine Learning group at TDB and SciLifeLab. The group develops theory, methods and software for data-driven science, with a current focus on uncertainty quantification
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-performance computing, particularly in the theory of efficient string and data structures, sequence indexing, and large-scale data analysis. Experience with the Burrows–Wheeler transform, pattern matching, and
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focus on Aerial and Space robotics. The vision of RAI is aiming in closing the gap from theory to real life, while the team has a strong expertise in field robotics. Specific application areas of focus
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application! Work assignments The project's contribution will lie at the intersection of random matrix theory and statistical inference theory, with applications in several fields of science. Special emphasis
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surfaces. This work builds on our ongoing, long-term research program centered on spin-split electronic structures of quantum wells—an area that has attracted significant attention in the context
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effort aimed at establishing new principles for electronic functionalization of living systems, where biological structure and signaling guide both the formation and operation of electronic materials
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simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization