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field (e.g. statistics, computer science, or quantitative biology). Experience in the application and development of computational methods/tools or machine learning algorithms. Good computer programming
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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and mathematical modeling, hierarchical statistical modeling, machine learning, remote sensing, geospatial statistics) • Demonstrated ability to conduct independent research and publish high-quality
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skills (one or more of the following strongly desired) Exploratory analysis of massive datasets (machine learning methods) Spatial data analysis and Geographic Information Systems (GIS) Forecasting and
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biosciences and data analyses. Qualifications Basic qualifications (required at time of application) PhD (or equivalent international degree) or enrolled in a PhD (or equivalent international degree) program
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environments, specifically Computer Vision, Machine learning algorithms and methods for rock characterization, fragmentation prediction, and mining optimization. Specific Requirements Good academic and
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machine learning methods. Provide theoretical predictions to guide experiments, and atomic-scale physical understanding to experimental observations. Publishing findings in peer-reviewed journals
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the Job related to staff position within a Research Infrastructure? No Offer Description The LIG brings together nearly 450 researchers, lecturers, PhD students and research support staff. The position
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Master’s degree in data science research, or a comparable domain. A strong background in machine learning (specifically generative models) and/or symbolic reasoning. Ability to conduct high quality academic