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Field
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Institute and RiverD International. The successful candidate will adapt existing and develop and test new methods for detecting metastatic lymph nodes based on their molecular signatures as captured by AF and
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, transportation, and more, with a special emphasis on grid resilience assessments and equity analysis. You will have the opportunity to creatively use interdisciplinary methods from computational data science
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Missouri University of Science and Technology | Rolla, Missouri | United States | about 22 hours ago
over time. This, key skills include familiarity with data management or large multi-method data sets and excellent data-analytic skills, especially those relevant to complex, large longitudinal studies
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plasma channels, and methods for controlling injection of electrons into laser-driven plasma wakefields. This work will be undertaken within the research groups led by Prof. Simon Hooker (Oxford), in
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improve the overall learning experience for students. The project seeks to promote lifelong learning by revolutionizing traditional teaching methods and providing innovative tools for interactive and
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quantum field theories, and the application of Hamiltonian methods to gauge theories, though you will also be encouraged to develop and pursue your own research directions. Applicants should have a PhD in
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simulation methods, decision theory, uncertainty quantification, machine learning. Applications and areas of key innovation Image analysis, computer graphics, autonomous and assisted driving, 3D scene analysis
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of phylogenomics to work with Professor Tiago Simões. The Simões lab is broadly interested in phylogenetic methods and applications, using morphological and genomic data for reconstructing evolutionary
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position in statistics, machine learning, and data science. The postdoctoral researcher will be mentored by Professor Yiyuan She and will contribute to the development of innovative statistical methods and
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Analyze and assess risks of de-anonymization, including researching current de-anonymization strategies Investigate synthetic data generation methods and their utility Deploying and benchmarking the above