Research Assistant Professor
Job no: 516035
Work type: Research Faculty
Senior management: Agriculture & Life Sciences
Department: Agricultural & Applied Economics
Location: Blacksburg Area
Categories: Agriculture / Life Science, Research / Scientific
The Center for Agricultural Trade (https://aaec.vt.edu/extension/Agricultural-Trade-Center.html) in the Department of Agricultural and Applied Economics (https://aaec.vt.edu/) at Virginia Tech invites applications for an early career, non-tenured position at the rank of research assistant professor with a specialization in global economic modeling and econometric analyses of agricultural trade and policy issues. The position is a 9-month, academic-year appointment with expectations for funding summer activities through external grants, teaching, or other mechanisms. Funding for this position is for two years with subsequent years dependent upon the availability of external research funds and performance evaluation. The anticipated start date for this position is August 10, 2021.
Research Appointment: The successful candidate will develop a strong, externally funded research program that employs computational global economic modeling and modern econometric approaches to evaluate international trade and policy issues relevant to U.S. and global agricultural industries. Specifically, the successful candidate will be expected to conduct ex ante forecast simulations and/or ex post econometric assessments of policy scenarios relevant to US and global agricultural trade and environmental issues including (but not limited to): (i) trade disputes, (i) non-tariff barriers, (iii) the impacts of climate change, carbon emission targets, and the structure of global trade and productivity, and (iv) bilateral and regional free trade agreement priorities. The successful candidate will work closely with the Center’s team of faculty and graduate students to maintain and update the Center for Agricultural Trade’s global linked livestock-crop and dairy source-differentiated partial equilibrium models.
Research applications will have the opportunity to contribute to a broad range of fields represented within the department, such as international trade, international economic development, food and health, environmental & natural resource economics, and agribusiness. The successful candidate is also expected to contribute to the College of Agriculture and Life Science's (CALS) Global Agricultural Productivity (GAP) Initiative (https://globalagriculturalproductivity.org/), and new Center for Advanced Innovation in Agriculture (CAIA) (https://caia.cals.vt.edu/). The successful candidate will also have opportunities to forge research ties with other units on campus, such as the College of Engineering, College of Natural Resources and the Environment, the Global Change Center, the interdisciplinary graduate education program (IGEP) in remote sensing, the Institute for Society, Culture, and Environment (ISCE), and VT's Advanced Research Computing (ARC) center.
Opportunity for Teaching & Advising: The individual will be expected to participate in the recruitment and advising/mentoring of graduate students, including serving on Master's theses and Ph.D. dissertation committees. The individual may also have the opportunity to teach a class at the undergraduate or graduate levels, depending on the candidate’s interests and needs of the department.
• PhD in Agricultural and Applied Economics, Economics, Computer Science, Operations Research, Management Science, Statistics, Engineering, or a closely related discipline
• Research experience and coursework related to quantitative economic modeling and applied econometrics.
• Record of peer-reviewed publications or advanced working papers related to the position description.
• Proficiency in GAMS, Python and/or Gempack modeling language.
• Experience in procuring grants or other external funding for programmatic support.
• Spatially explicit research experience involving regional and global economic and policy modeling.
• Strong communication and interpersonal skills to work effectively in a team-based atmosphere.
• Experience working large datasets and calibration of simulation models.
• Teaching experience or service as a graduate student teaching assistant.
Commensurate with Experience
The successful Candidate will be required to have a criminal conviction check
Dedicated to its motto, Ut Prosim (That I May Serve), Virginia Tech pushes the boundaries of knowledge by taking a hands-on, transdisciplinary approach to preparing scholars to be leaders and problem-solvers. A comprehensive land-grant institution that enhances the quality of life in Virginia and throughout the world, Virginia Tech is an inclusive community dedicated to knowledge, discovery, and creativity. The university offers more than 280 majors to a diverse enrollment of more than 36,000 undergraduate, graduate, and professional students in eight undergraduate colleges, a school of medicine, a veterinary medicine college, Graduate School, and Honors College. The university has a significant presence across Virginia, including the Innovation Campus in Northern Virginia; the Health Sciences and Technology Campus in Roanoke; sites in Newport News and Richmond; and numerous Extension offices and research centers. A leading global research institution, Virginia Tech conducts more than $500 million in research annually.
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.
If you are an individual with a disability and desire an accommodation, please contact Dr. Jason Grant at firstname.lastname@example.org during regular business hours at least 10 business days prior to the event.
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