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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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. 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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, organizational and time management skills with ability to execute program plans • Proficient computer skills such as email tools, data entry, and manipulating Excel spreadsheets • High comfort level with 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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diverse range of individuals, learn from experienced professionals, and contribute to the success of both the office and the university. Important Points 1) You will be joining the Office of the University
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to successful work-based learning and internship support services. The Professional Development and Experiential Programs team conceptualizes and executes programs, services, and resources that help Virginia Tech
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and collaborate around related scholarship (https://honorscollege.vt.edu). Virginia Tech Honors provides students with transdisciplinary and experiential learning opportunities through immersive courses
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. Demonstrated computer proficiency with Microsoft Outlook, Excel, and adaptable at learning systems. Excellent time management skills and works independently. Preferred Qualifications Experience providing support
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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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, university policy, and legal compliance. Required Qualifications · Bachelor's degree in web development, web design, information science, computer science, instructional design, learning technology, assistive