Tenure-Track Statistics Faculty Search
The Virginia Tech Department of Statistics 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 creation of the university’s “Data Analytics and Decision Sciences” destination area. He or she will also have the opportunity to work in the area of cyber analytics and cybersecurity, where Virginia Tech is leading the state of Virginia’s Cyber Initiative. This is a unique 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: 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 provides a complete description of faculty responsibilities.
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 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.
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.
Questions regarding the position can be directed to Professor Robert B. Gramacy (email@example.com), chair of the search committee. Review of applications will begin on December 1, 2018. As part of the hiring process, the successful applicant must pass a criminal background check.
Applications must be submitted online at http://listings.jobs.vt.edu (TR018011) and should include a cover letter; curriculum vitae; research plan; teaching statement; and a statement of any previous activities aimed at expanding the diversity and/or mentoring of minorities, women, or members of other underrepresented groups, including how the applicant will further Virginia Tech’s commitment to build a culturally diverse educational environment. Applications must also include contact information for at least three references.
Virginia Tech is an EO/AA university, and offers a wide range of networking and development opportunities to women and minorities in science and engineering. Virginia Tech is committed to a culturally and ethnically diverse campus environment and to principles that promote inclusive practices. More information is available at http://www.inclusive.vt.edu/index.html.
Virginia Tech recognizes that meeting the needs of today’s professional couples is a key factor in recruiting and retaining new faculty and has established a dual career office to bring a new level of support to couples and their families within the university (www.hr.vt.edu/jobs/dual-career.html).The Department of Statistics offers a supportive environment, including a mentoring program, to its junior faculty. Individuals with disabilities desiring accommodations in the application process should notify Ms. Betty Higginbotham, Department of Statistics, Tel: (540) 231-5657, Email: firstname.lastname@example.org.
Applications are invited for a full-time, non-tenure track instructor position in the Department of Statistics. The timeline for this invitation is open-ended, and applications will be reviewed as they are received.
Primary duties for this position are to teach a variety of statistics courses, assist with the coordination of multi-section introductory statistics courses, and serve as an academic advisor for undergraduate students. Continuing development of own professional capabilities and service on departmental, college, and/or university committees is expected. Participation in local, national and international statistical societies is encouraged, especially with regard to efforts in statistical education.
- M.S. from an accredited university in Statistics or a related field
- Teaching Experience
- Demonstrated excellence in teaching with the use of modern pedagogical techniques