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of Engineering as well as the new Virginia Tech Institute for Advanced Computing (IAC) located in the Greater Washington, D.C. area. The position involves conducting research in signal processing, machine learning
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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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broad range of research areas. We are interested in an experimentalist who can develop large datasets in support of emerging artificial intelligence and machine learning driven advances in fluid dynamics
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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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. The individual is expected to work collaboratively with multidisciplinary teams from academia, government, and industry, and serve as PI/Co-PI on projects. Required Qualifications • Successful completion of a PhD
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for construction operations. The successful candidate will contribute to cutting-edge research in mixed reality (MR)-based simulation platforms, machine learning-based process optimization, and human-machine
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be the lead for an advanced manufacturing experiential learning and research space in our new Mitchell Hall which is currently under construction on the Virginia Tech campus. This will include training
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Professor in Human-Computer Interaction (HCI) and related areas. Virginia Tech was founded in 1872 as a land-grant institution and has been long recognized for its tradition of innovation and public service
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had expenditures of approximately $24 million in research during the 2024-2025 academic year. Required Qualifications A PhD or equivalent at the time of appointment in a discipline closely related to
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professional references with complete contact information. Required Qualifications A PhD degree in electrical engineering, computer engineering, computer science, or a closely related field completed prior