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tenured and tenure-track faculty with wide-ranging research interests, and strong research groups in cybersecurity, systems and networks, machine learning and data mining, artificial intelligence, robotics
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and Technology (IST) includes 45 full-time faculty with research spanning artificial intelligence, cybersecurity, data mining, machine learning, natural language processing, human-centered computing
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teaching-track faculty and 54 tenured and tenure-track faculty with wide-ranging research interests, and strong research groups in cybersecurity, systems and networks, machine learning and data mining
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; cybersecurity; digital forensics; machine learning and artificial intelligence; semiconductors and microelectronics; power and energy systems; signal processing; and space-based and satellite systems. About the
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; Excellent computer skills and aptitude to learn new software; Demonstrated design, layout, editing, and proofreading skills; Demonstrated organizational, time management, and problem solving skills; Skill in
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: The Department of Information Sciences and Technology (IST) includes 45 full-time faculty with research spanning artificial intelligence, cybersecurity, data mining, machine learning, natural language processing
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are available at http://ist.gmu.edu/students/current-students/course-syllabi/) : Graduate Big Data Analytics Natural Language Processing Machine Learning Data Mining Cybersecurity Cyber-Human Systems Artificial
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Engineering Building (LSEB) was opened in the SciTech campus. LSEB houses a number of ME laboratories and machine shop used for undergraduate teaching. The department offers a bachelor’s in Mechanical
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systems; and Ability to learn new software. Instructions to Applicants: For full consideration, applicants must apply for Administrative Specialist, Alumni Relations at https://jobs.gmu.edu/. Complete and
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school in the United States. This provides students with direct and personalized interaction with professors – an invaluable advantage when learning complex legal doctrines. Although still a relatively