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machine-learning tools. Data analyzed include precursors such as volatile organic compounds, aerosol number and mass concentrations, chemistry, biological particles, cloud and ice condensation nuclei, light
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techniques are oftentimes needed to correctly describe correlated electronic systems. In recent years, electronic structure methods have been coupled to machine learning to accelerate predictions and for
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to PET image reconstruction. Highly specialized and technical expertise in scientific programming, medical imaging and machine learning methods is required to carry out designated research projects
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development opportunities, including a membership to Academic Impressions, LinkedIn Learning, and UT Dallas Bright Leaders Program. Visit https://hr.utdallas.edu/employees/benefits/ for more information
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description A postdoctoral researcher
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mechanics, finite element modeling, and scientific machine learning. The RSE will contribute to the design, implementation, and maintenance of open-source software libraries that integrate phenomenological
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students and postdoctoral researchers on experimental methods. Teach a course in fundamental fluid mechanics. Contribute to laboratory health and safety activities. Write journal papers. Required selection
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provides comprehensive and competitive medical and dental care programs, generous retirement benefits, and a wide array of family-friendly and cultural programs to eligible team members. Learn more at https
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California State University, Long Beach | Long Beach, California | United States | about 13 hours ago
Department of Biological Sciences Required Qualifications: • Ph.D. in marine biology, zoology, ecology, evolution, or a related field • Postdoctoral research experience or equivalent • Research experience in
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graphs and related structures, limit theorems, stochastic calculus and applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department