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of sparse matrix, tensor and graph algorithms on distributed and heterogenouscomputational environments. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational Science, or related
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) mathematics, physics, or a related STEM fields. Job Description: The Division of Biomedical Informatics and Data Science (BIDS) within the Department of Health Outcomes and Biomedical Informatics (HOBI
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plus Education and training PhD in Bioinformatics or in Biology, Machine Learning, Statistics, Physics, Mathematics, Chemistry or related areas Languages: Highly proficient in both spoken and written
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computer science, mathematics, computer engineering, or relevant technical field First-author peer-reviewed published papers (or under review) Proficient programming experience in Python and libraries (e.g., Pytorch
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Prof Anuj Srivastava (Florida) and their teams. The project concerns methods for the statistical modelling of shapes changing in time, and involves mathematical and statistical techniques from
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research and teaching in applied analysis, computational mathematics, data science, discrete mathematics, financial mathematics, stochastics, and statistics. The Department of Applied Mathematics is the only
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Sciences | College Administration Natural and Mathematical Sciences The Post Doctoral Scholar will play a key role in advancing interdisciplinary research in STEM higher education to evaluate student
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Computer Science, Information Theory, Physics or related fields High level of mathematical maturity Experience with topics related to quantum LDPC codes and decoding algorithms, or demonstrated ability and
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, information science, data science, (bio)-statistics, (applied) mathematics, physics, or a related STEM fields. Strong programming and data analysis skills (e.g., Python, R) Solid understanding of machine learning, deep
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PhD or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological science with demonstrated experience in computational and statistical