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Virginia Tech

Location: BlacksburgVA 24061 Document ID: AC365-0NGI Posted on: 2018-11-0611/06/2018 Job Type: Full-time

Job Schedule:Full-time
2018-12-06
 

Assistant Professor

Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic information, national origin, political affiliation, race, religion, sexual orientation, or veteran status, or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees or applicants, or on any other basis protected by law.

Position Summary:
The Virginia Tech Department of Statistics (www.stat.vt.edu) invites applications for a tenure track faculty position in Statistics to begin in August 2019. Appointment at the rank of assistant professor is preferred, but the associate level will be considered for exceptional candidates. Requirements include a Ph.D. in statistics or a closely related field and a research focus in data analytics, statistical/machine learning, artificial intelligence, cyber analytics, data mining, stochastic modeling/inference, or any related branch of computationally intensive statistical methods.
This position is part of a major emphasis on statistics, including computational modeling, data science and analytics, and empirical decision making and is in support of the Computational Modeling and Data Analytics ( CMDA ) program (www.ais.science.vt.edu/programs/cmda.html) at Virginia Tech. CMDA , a multi-department effort including not just Statistics but also the Departments of Mathematics and of Computer Science, represents an entirely new approach to training quantitative scientists, one that develops foundations for, knowledge of, and skills in computationally intensive techniques for modeling and inference.
Successful applicants will have the opportunity to be key players in the university's "Data Analytics and Decision Sciences" destination area (www.provost.vt.edu/destination-areas/da-overview/da-data.html). This is a unique opportunity to help develop and grow a truly innovative approach to education and research. He or she will also have the opportunity to work in the area of cyber analytics and cyber security, where Virginia Tech is leading the state of Virginia's Cyber Initiative: https://vtnews.vt.edu/articles/2018/06/cyber-initiative.html. Applications from researchers whose work and goals straddle traditional academic boundaries are especially encouraged.
Expectations for this position include: developing and maintaining a visible and vigorous funded research program; providing effective instruction and advising to a diverse population of undergraduate and graduate students; continuing development of professional capabilities and scholarly activities; curriculum development; participation in department, academy, college, and university governance; and professional service. The faculty handbook (available at http://www.provost.vt.edu) provides a complete description of faculty responsibilities.

Required Qualifications:
Applicants must have a strong background in computationally-intensive statistics with specialization in data analytics, machine learning, data mining, stochastic modeling/inference, interactive data visualization, high performance computing or some other area of computationally-intensive statistical methods; a strong promise for developing a well-funded and distinguished research program; demonstrated experience with and commitment to interdisciplinary research; willingness to cross disciplinary boundaries to tackle complex scientific challenges; a desire to advise and teach a student body that is diverse with respect to socio-economic status, demographics, interests, and abilities; and commitment/sensitivity to address issues of diversity in the university community. Applicants must have earned a doctorate in a relevant discipline at the time of appointment

Preferred Qualifications:
Preference will be given to candidates with demonstrated interest in interdisciplinary scholarship employing statistical and data analytical techniques. Preference will also be given to assistant professor candidates, candidates with postdoctoral or similar experience, and candidates with a record of achievement as might be demonstrated during a postdoctoral or similar appointment



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