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python), computational mechanics and fluid dynamics, numerical methods, machine learning, and the finite element method. In this position, the candidate will develop data-driven multiscale/multiphysics
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> on research projects related to kinetic equation and its related multiscale model reduction, numerical method, and data-driven/machine-learning approaches, including: • Numerical simulation of kinetic
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, numerical method, and data-driven/machine-learning approaches, including: · Numerical simulation of kinetic equations: fast algorithms with stability/convergence analysis; · Data-driven and machine
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structural motif analysis and the development of AI agent systems for scientific discovery. The position focuses on developing machine learning methods for RNA structure prediction, building agentic AI
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an on-call. On-Call allowed to work 1039 hours in any twelve (12) month period. Unit Position Summary The Data Scientist will develop and apply statistical, machine-learning, and computational methods
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group is looking for a researcher with expertise in machine learning, software development, and data analysis for work supporting the IceCube data-analysis pipeline and analysis of IceCube data. The
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-preserving, machine-learning–accelerated scientific computing for plasma physics applications. In particular, the project involves developing data-driven collisional kinetic models and numerical schemes
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conduct collaborative research at the intersection of non-canonical kinetic theory, non-conventional plasma turbulence modeling, high-speed flows, scientific machine learning for multi-physics problems
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trials to multi-omics explorations, imaging, biomarker development, stable-isotope tracers, and large-scale data set s and machine learning. The lab is now recruiting for a postdoctoral researcher, who
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. Bioinformatics analysis of data will include use of population genomics techniques, phylogenetics and machine-learning development. The successful candidate will be expected to write up results as manuscripts