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, observations, a hierarchy of numerical models, and machine-learning methods to understand their formation, dynamics, and predictability. The successful candidate will have substantial freedom to develop
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Models Basic Qualifications: A Ph.D. or equivalent degree in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong
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such as transformers, self-supervised learning, multimodal learning, generative models, graph neural networks, or foundation models. Experience with structural and/or functional brain modeling. Familiarity
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avenues of research rather than a required work plan. They may be pursued individually or in combination, and we welcome other creative and strategic approaches. Statistical or machine-learning approaches
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 12 days ago
); and propagation of physics-traceable soil moisture uncertainty into precipitation estimates. Machine learning approaches are welcome as tools within this framework — for example, learning the space–time
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. The postdoctoral scholar will contribute to an exciting research program within the Foy Lab (www.foylab.xyz/) https://foylab.xyz/ , developing machine learning, computational, and mathematical models for improving
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Learning: Apply shape-correspondence and machine-learning approaches, including spectral/graph-based surface alignment and autoencoder-based shape extraction, to compare limb and joint morphology across
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, gender identity, or gender expression. To learn more about diversity at the U: http://diversity.umn.edu Employment Requirements Any offer of employment is contingent upon the successful completion of a
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, together with research experience in at least one class of modern generative models, are particularly encouraged to apply. In addition to conducting research, the successful candidate will teach two courses
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Researcher position. The selected candidate will work on research and development for testing, analysis, and integration of AI and machine learning (ML) algorithms in new simulation and training architectures