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Instructional Faculty Search
The Virginia Tech Mathematics Department invites applications for full-time instructor and/or instructional faculty members of other ranks to start in the Fall of 2019. The initial appointment will be one-year restricted appointment, with possibility of renewal. The primary duties are teaching undergraduate classes. For some of the positions, these classes may include classes for pre-service teachers or Math Emporium classes. A master’s degree in mathematics or a master’s degree in a related field with a concentration in mathematics is required. Exceptionally qualified candidates may be considered for other instructional positions or for different terms of appointment. To view a full job description and position qualifications and to apply, go to www.jobs.vt.edu and search for job posting TR0190054.
Applications must be submitted online at www.jobs.vt.edu, posting number TR0190054. Online applications include a resume/CV, cover letter, and candidate statement addressing the candidate’s teaching philosophy. Each applicant should follow the instructions in the online application system to request that two references submit letters of recommendation, or letters can be emailed to email@example.com. Additional information about position requirements and responsibilities can be found at www.hr.vt.edu or www.math.vt.edu. The faculty handbook (available at www.provost.vt.edu) gives a complete description of faculty responsibilities. As part of the hiring process, the successful applicant must pass a criminal background check. Questions about the search may be addressed to firstname.lastname@example.org.
Applications received by May 28, 2019 will receive full consideration; however, applications will be accepted until positions are filled.
Virginia Tech is an Equal Opportunity / Affirmative Action Institution. 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. For inquiries regarding non-discrimination policies, contact the executive director for Equity and Accessibility at 540-231-8771 or Virginia Tech, North End Center, Suite 2300 (0318), 300 Turner St. NW, Blacksburg, VA 24061.
Individuals with disabilities desiring accommodations in the application process should notify Leigh Ann Teel, email@example.com, 540-231-6536) or call TTY 1-800-828-1120 by the application deadline.
Virginia Tech is the recipient of a National Science Foundation ADVANCE Institutional Transformation Award to increase the participation of women in academic science and engineering careers. Additionally, Virginia Tech is supportive in addressing the needs of dual-career couples.
May 20-24 2019: Conference on Mathematical Physics at the Crossings, in celebration of George Hagedorn's 65th Birthday.
CREDIT-BY-EXAM: Sign-up times for credit-by-exam, SPRING 2019
Monday, MAY 6 - Wednesday MAY 8 10:00 AM - 11:30 AM and 1:30 PM - 3:00 PM
FORCE ADD SURVEY: The Math Department will open its force add survey for FALL 2019 on August 3, 2019. If you need to request a force add, please check our website on or after that date for the survey link.
Featured ResearchJulianne Chung - Inverse problems arise in many applications, such as biomedical imaging, astronomy, and geophysics. For example, in image deconvolution, the observed image is degraded by blur and noise, and the goal of the inverse problem is to reconstruct an approximation of the true image. A significant challenge is that the inverse problem is ill-posed, meaning small errors in the data can result in large errors in the solution. By incorporating prior information, regularization is a standard approach to modify the problem and overcome the inherent instability of the problem. Dr. Chung's research is to develop regularization methods and software for computing solutions to inverse problems.
The figure shows an example from image deconvolution. The blur function and observed image are provided, and regularization was used to obtain the reconstructed image.
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