Working Title:

Open Rank Professor - Messy Data

Department:

Statistics IANR-0832

Requisition Number:

F_230028

Posting Open Date:

02/17/2023

Application Review Date: (To ensure consideration, please submit all application materials before review date):

03/27/2023

Open Until Filled:

Yes

Description of Work:

The Department of Statistics at the University of Nebraska-Lincoln (UNL) Institute of Agriculture and Natural Resources (IANR) is seeking applications for a specialist in messy data. This is a 9-month (academic year), tenure-track appointment. The incumbent, as an expert in applied statistics, will contribute to the integrated research and teaching land-grant mission of the home unit and IANR, as an effective scholar and citizen, including supporting student recruitment and IANR science literacy. As appropriate to the appointment, the incumbent will develop a high-impact, nationally and internationally recognized research and teaching program in the theory, methodology, and practice of analyzing complex data sets of interest to IANR-related fields. The apportionment is 50% research and 50% teaching. This position will be located in Lincoln, Nebraska.

Recognizing that diversity enhances creativity, innovation, impact, and a sense of belonging, the Institute of Agriculture and Natural Resources (IANR) and Statistics are committed to creating learning, research, Extension programming, and work environments that are inclusive of all forms of diversity. Consistent with the University’s N2025 Strategic Plan, we see every person and every interaction as important to our collective well-being and our ability to deliver on our mission.
Specific research duties (.50 FTE) include:

• Working/collaborating with other researchers who are collecting data in the fields of agronomy, crop science, soil science, animal science, natural resources science, and related areas to ensure maximum information can be extracted from them. These analyses may require the development of novel techniques for data that does not comfortably fit standard models. The successful applicant will be skilled in at least one of the following areas: missing data, un-replicated data, observational data, augmented experimental design in the context of complex data, multitype data, improperly sampled or measured data, dependent data, or a related complex data type.
• Connecting with stakeholders, agencies, and/or industry partners to strengthen research and educational programming; effectively obtaining and leveraging external and internal support (grants, fee revenue, etc.) for research and teaching activities; mentoring undergraduate and graduate students; publishing in high-quality, high-impact peer-reviewed journals, and participating in scientific meetings and other appropriate professional activities; and translating research-based information into learner-centered products.

There will also be opportunities for fieldwork to ensure the collection of high-quality data and to collaborate closely with subject matter experts to ensure the analyses address problems that are both urgent and important.
Teaching duties (.50 FTE)
The incumbent will be expected to teach up to four courses per academic year at the undergraduate, Master’s, or Ph.D. level with topics appropriate to the incumbent’s expertise. One or more of these courses will be online and part of the teaching apportionment may include helping to develop the Department’s online course offerings.

Other teaching duties include:

• Creating scholarly, innovative, and high impact learning programs and tools.
• Mentoring colleagues through professional development, translational research, grants, and professional writing.
• Contributing to program and curriculum improvement.

As an EO/AA employer, the University of Nebraska considers qualified applicants 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 https://www.unl.edu/equity/notice-nondiscrimination.

Minimum Required Qualifications:

• Ph.D. in statistics, data science, computer science, engineering, or a closely related field.
• Experience with consulting or statistical applications in a field of importance to IANR.
• Computational proficiency.
• Excellent communication skills.

Preferred Qualifications:

• Experience/interest in collaborating with domain experts (Agronomy, Animal Sciences, Entomology, Natural Resources, Plant Pathology) in fields of importance to IANR on agricultural and natural resources data analysis.
• Evidence of ability and interest in modern analysis techniques for complex and challenging data.
• Teaching experience at the university level.

How to Apply:

Click “Apply to this Job” and complete the information form. Attach the following documents:

1. A letter of interest that describes your qualifications for the job, anticipated contributions, and your experience working in diverse teams or groups and your anticipated contributions to creating inclusive environments in which every person and every interaction matters (2 page maximum). See https://ianr.unl.edu/tips-writing-about-commitment-to-deib for guidance in writing this statement.
2. Your curriculum vitae.
3. Contact information for three professional references.

For questions or accommodations related to this position contact:

Jodi Mackin
jmackin1@unl.edu

Job Category (old):

Faculty Tenure/Tenure Leading

Job Type:

9 Month

Position funded by grant or other form of temporary funding?:

No