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models and machine learning techniques for kinetic equations arising from plasma and neutron transport. The position will be based at Virginia Tech’s campus in Blacksburg, VA. The postdoc will have a
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. Preferred Qualifications • Experience with deep learning architectures applied to geophysical or environmental data. • Familiarity with physics-informed machine learning or hybrid modeling approaches
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is looking for a highly motivated and independent individual to work as a Postdoctoral Associate. This position is for a post-PhD trainee preparing for a research scientist career path. The planned
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simulations), computational chemistry, machine learning, enhanced sampling methods, force field development, or related fields are encouraged to apply. Start date is flexible. the postdoc will collaborate with
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We are seeking a highly motivated and skilled individual to join our neuroimaging laboratory, which specializes in multimodal image fusion, multiparametric modeling, and machine learning techniques
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candidate with expertise in the following four areas: (1) working with large-scale digital trace data; (2) building and running natural language processing and machine learning workflows; (3) experimental
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scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and machine learning
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combination of observational data, machine learning techniques, and cosmological simulations. The group is actively involved in multiple JWST Guaranteed Time Observation (GTO) and General Observer (GO) programs
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machine learning. Essential Duties and Responsibilities: Develop and implement advanced reconstruction algorithms for correlated and low-dose imaging modalities. Maintain and extend Python-based software
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studying deformation mechanisms in refractory alloys as via atomic-scale calculations as well as application of machine learning to materials discovery The ideal candidate will have the following