420 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions at Virginia Tech
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pregnancy), gender, gender identity, gender expression, genetic information, ethnicity or national origin, political affiliation, race, religion, sexual orientation, or military status, or otherwise
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pregnancy), gender, gender identity, gender expression, genetic information, ethnicity or national origin, political affiliation, race, religion, sexual orientation, or military status, or otherwise
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administrative experience. Preferred Qualifications Previous administrative support experience; B.S. or B.A. in any field; Knowledge of university software systems (e.g. Banner, Canvas or similar learning
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, InDesign, Acrobat, etc.). - Experience providing support for meetings and/or events . - Experience with a learning management system (LMS) like Canvas, Blackboard, etc. and/or html . Pay Band 3 Overtime
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. This appointment is 30% research, 50% teaching, and 20% Extension. In addition, the successful candidate will contribute to ALCE as a team member in transforming lives through learning, discovery, and engagement
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programs in Construction Engineering and Management (ABET-accredited) and Building Construction (ACCE-accredited). The successful candidate will be required to teach 3-4 courses per semester in the School
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Job Description The Virginia Tech Institute for Advanced Computing (IAC), in partnership with the Departments of Computer Science (CS) and Electrical and Computer Engineering (ECE), invites
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demonstrating a solid understanding of academic office operations Familiarity with Banner or similar administrative systems is highly desirable. Experience with or willingness to learn basic web page creation and
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of the human impacts of AI, organizational security, employment, and communication in evolving workplaces. Candidates for this position will be expected to teach at the undergraduate and graduate (master’s
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control, Large-scale or distributed optimization for complex aerospace systems, Machine learning, reduced-order modeling, surrogate modeling, and data-driven approaches with applications to aerospace