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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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. At the Division of Systems and Control , we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and
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completed courses. Earlier specialization in machine learning, control theory or mathematics is highly desirable. After the qualification requirements, great emphasis will be placed on personal skills. Target