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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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sequencing data quality control within an interdisciplinary setting. This project is particularly suited to candidates with a strong interest in experimental method development and optimisation
Searches related to control theory
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