Data Management Specialist Research Assistant Professor

Employer
Agricultural Research Division (ARD) at the Institute of Agriculture and Natural Resources, University of Nebraska-Lincoln
Location
Nebraska, United States
Salary
Salary Commensurate with experience
Posted
Mar 26, 2021
Employment Level
Non-Tenured Track
Employment Type
Full Time

The Agricultural Research Division (ARD) at the Institute of Agriculture and Natural Resources, University of Nebraska-Lincoln (https://ard.unl.edu) invites applications for a Data Management Specialist Research Assistant Professor. This position will support ARD’s current efforts around big data, and the successful candidate will contribute to the cutting-edge transdisciplinary research in agriculture and natural resource sciences. The candidate will work with the Holland Computing Center, Information Technology Services, University Libraries and external federal partners (e.g., Agricultural Research Service, USDA) to initiate the development of agricultural data architecture, configuration, and analytic technologies that will allow our researchers to store (short and long-term), visualize, show workflow, use version control (i.e., keep previous versions), integrate heterogeneous data, capture metadata (that can be expandable), guarantee compatibility between systems, and offer onsite analysis (i.e., avoid downloads). In addition to the above-described duties, the candidate will be expected to accept committee assignments, reporting responsibilities, and other special ad hoc assignments. The candidate must also demonstrate a commitment to contributing to a culture that supports diversity and inclusion.

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) and Agricultural Research Division are committed to creating learning, research, extension programming, and work environments that are inclusive of human diversity. We actively encourage applications from and nominations of individuals from underrepresented groups.

A PhD in computer science, geospatial science, machine learning, agricultural sciences or a related field is required. The successful candidate must have experience in data infrastructure design and development; and experience in working with large-scale unstructured datasets: time series, geospatial (vector and raster), images, and video streams.

Preferred candidates will have an understanding of agricultural and natural resources data; demonstrated ability to multitask; oral and written communication skills; student/staff training ability at the college level; and experience in publishing technical papers, reports and other products (such as software packages and apps).