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to scientific computing and other scientific domains. Candidates with strong backgrounds in stochastic analysis, numerical analysis, sampling methods, optimization, scientific computing, or related areas
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methods (e.g., variational autoencoders) applied to biomolecular dynamics data. ● Familiarity with elastic network modeling (ANM/GNM) or the ProDy software package. ● A track record of independent
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) and contribute to LU-PCG’s research under the U.S. Department of Energy’s (DOE) GENESIS Mission. Research will involve developing fast neural surrogate models, state estimators/virtual sensors, multi
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regional agencies, a robust methodology for designing flood-retention basins and dams. We are looking for a candidate to test and develop statistical and simulation-based methods for estimating design floods
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, including appropriate controls, statistical approaches, and methods to ensure reproducibility and scientific rigor. · Independently interpret experimental results, troubleshoot technical and
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statistical and simulation-based methods for estimating design floods for reservoirs and dams with return periods of up to 10.000 years. The tasks include adapting extreme value statistics methods using spatial
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of single-cell analysis methods to investigate quiescence at individual cell and population levels. Where to apply E-mail [email protected] Requirements Research FieldBiological
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and economy that respect people and their environment. We are looking for our next postdoctoral researcher in computer graphics and machine learning to join the Image, Data and Signal (IDS) department
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research in the natural, mathematical and computer sciences with a focus on the processing, structuring, and analyzing of large amounts of complex data and the development of computational methods and
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research using mixed methods, qualitative and quantitative research designs including interviews, focus groups, surveys, and experiments, analyzing quantitative and qualitative data, publishing in leading