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Virginia Tech

Location: BlacksburgVA 24061 Document ID: AB282-0OQT Posted on: 2018-02-1402/14/2018 Job Type: Full-time

Job Schedule:Full-time
2018-03-16
 

Statistician

Virginia Tech is a public land-grant university, committed to teaching and learning, research, and outreach to the Commonwealth of Virginia, the nation, and the world. Building on its motto of Ut Prosim (that I may serve), Virginia Tech is dedicated to InclusiveVT-serving in the spirit of community, diversity, and excellence. We seek candidates who adopt and practice the Principles of Community, which are fundamental to our on-going efforts to increase access and inclusion, and to create a community that nurtures learning and growth for all of its members. Virginia Tech actively seeks a broad spectrum of candidates to join our community in preparing leaders for the world.

Position Summary:
The Virginia Tech Department of Statistics (www.stat.vt.edu) invites applications for an open rank tenured or tenure track faculty position in Statistics to begin in August 2018. Requirements include a Ph.D. in statistics or a closely related field; a research focus in data analytics, statistical/machine learning, data mining, stochastic modeling/inference, interactive data visualization, or any related branch of computationally intensive statistical methods; and teaching experience.
This position is part of a major emphasis on statistics, including computational modeling, data science and analytics, and empirical decision making at Virginia Tech. This position will support of the Computational Modeling and Data Analytics ( CMDA ) program (www.science.vt.edu/ais/cmda). 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 also have the opportunity to be key players in the creation of the university's "Data Analytics and Decision Sciences" destination area (http://provost.vt.edu/destination-areas/da-data.html). This is a rare opportunity to help develop and grow a truly innovative approach to education and research. Applications from researchers whose work and goals straddle traditional academic boundaries are especially encouraged.
Expectations for this position include: 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 statistics with specialization in data analytics, machine learning, data mining, stochastic modeling/inference, interactive data visualization, high performance computing and computationally intensive statistical methods; strong promise for developing, or in the case of senior applicants continuing, a well-funded, internationally 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, 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 examples of interdisciplinary scholarship employing statistical and data analytical techniques. Preference will also be given to candidates with postdoctoral or similar experience, with a record of achievement as might be demonstrated during a postdoctoral or previous faculty appointment.



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