The Institute of Agriculture and Natural Resources (IANR) at the University of Nebraska-Lincoln (UNL) is seeking applications for a Sequential Decision Making Professor. Starting in August 2022, this appointment is a 9-month, tenured or tenure-track position at the Assistant or Associate Professor level with apportionments in either or both the Department of Biological Systems Engineering and the Department of Statistics.

Recognizing that diversity within a context of inclusivity enhances creativity, innovation, impact, and a sense of belonging, the Institute of Agriculture and Natural Resources (IANR), Biological Systems Engineering and Statistics are committed to creating learning, research, Extension programming, and work environments that are inclusive of all forms of human diversity. We actively encourage applications from and nominations of qualified individuals from underrepresented groups.
The successful candidate will develop a high-impact, nationally and internationally recognized research and teaching program in sequential decision making in response to real time data collection. Example data sources include sensors or other automated observational data systems. This includes creating new techniques (Bayesian, frequentist, computational, etc.) to extract the maximum value from data streams generated for the management of agricultural and natural resource systems. The incumbent is expected to collaborate with current faculty in both the agricultural/natural resource sciences and the computational/statistical sciences. Research duties for this position include field work with extension teams, subject matter experts and other partners. Consistent with the role and mission of the departments, the appointee is expected to seek sources of external funding to help support the research program.
In addition, the successful candidate will support the recruitment, funding, and training of undergraduate and graduate students. The usual teaching load will be three standard courses per year, or equivalent, as assigned by the department chair(s). Specific course assignments may be changed over time according to academic units’ needs. 

The appointee will also contribute, as an effective scholar and citizen of a land-grant institution, to the integrated mission of home units (e.g., department, center), including supporting student recruitment, IANR science literacy initiative, and beyond. Additional responsibilities of the academic appointment are to participate in retention and placement activities and teaching outcomes assessment, instructional improvement, and teaching scholarship. 

Minimum qualifications:
• Ph.D. in statistics, data science, engineering, or closely related field.
• Written work in sensor data analysis, sequential decisions, streaming/observational data or closely related areas.
• Computational proficiency such as coding or algorithm design.
Preferred qualifications:
• Demonstrated practical experience in statistics, data science, engineering in a related field of importance to IANR.
• Excellent communication skills.
• Ability to engage in scientific teamwork to address major issues.
• Teaching experience at the university level.
• Interest in working with diverse or underrepresented communities or groups.
Review of applications will begin on January 17, 2022 and continue until the position is filled or the search is closed. To view details of the position and create an application, go to, requisition F_210219. Click “Apply to this job” and complete the information form. Attach 1) a letter of interest that describes your qualifications for the job, anticipated contributions, and the value you place on diversity and your anticipated contributions to creating inclusive environments in which every person and every interaction matters (2 page maximum; see for guidance in writing this statement); 2) your curriculum vitae; 3) a teaching and research statement (1 page each, combined into one document); and 4) contact information for three professional references.

As an EO/AA employer, qualified applicants are considered for employment without regard to race, color, ethnicity, national origin, sex, pregnancy, sexual orientation, gender identity, religion, disability, age, genetic information, veteran status, marital status, and/or political affiliation. See