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Field
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learning algorithms. We combine statistical methods with online reinforcement learning algorithms to develop reinforcement learning algorithms and inferential tools. The successful applicant will be expected
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the development of fast and scalable algorithms for many-component systems, and of coarse-grained models that can be analyzed and simulated. Strong applicants with backgrounds in applied and computational
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Chemistry o Combinatorics, Algorithm, Extremal Graph Theory, Computing Theory o Programming Language, AI Theory or Machine Learning o Classical and Quantum Algorithm for Computational Quantum Many-body Theory
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Computational Condensed Matter Physics and Materials Sciences o Theoretical and Computational Biophysics o Soft Matter Physics o Physical Chemistry and Theoretical Chemistry o Combinatorics, Algorithm, Extremal
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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, inaccessible to standard techniques. To probe such regimes requires the development of fast and scalable algorithms for many-component systems, and of coarse-grained models that can be analyzed and simulated
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and/or scientific computation, scientific software and algorithm development, data analysis and inference, and image analysis Ability to do original and outstanding research in computational biology
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& Other Requirements Demonstrated abilities in mathematical modeling, analysis and/or scientific computation, scientific software and algorithm development, data analysis and inference, and image analysis
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, analysis and/or scientific computation, scientific software and algorithm development, data analysis and inference, and image analysis Ability to do original and outstanding research in computational biology
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application to lineage tracing Algorithms for characterizing structural alterations in bulk and single cell whole-genome data Mutational signature analysis for cancer/brain samples Analysis of repetitive