Description
The University of Alabama School of Library and Information Studies (SLIS) is seeking a full-time, nine-month, non-tenure track, renewable contract instructor or assistant professor in applied informatics to join our engaged faculty of creative scholars, innovative researchers, and award-winning educators. We are at an exciting juncture with a new building, growing MLIS program, and a new undergraduate informatics major. Below is more information about the position.
Renewable Contract Faculty Member – Applied Informatics (Analytics and Design) at the level of instructor (requires master’s degree) or assistant professor (requires PhD) to begin August 16, 2026. This teaching focused position is based on a 3-year renewable contract cycle with opportunities for promotion.
Links to Apply and for Additional Information on the Position:
Instructor, renewable contract: https://careers.ua.edu/jobs/instructor-renewable-contract-applied-informatics-school-of-library-information-sciences-tuscaloosa-alabama-united-states
Assistant Professor, renewable contract: https://careers.ua.edu/jobs/assistant-professor-renewable-contract-school-of-library-information-sciences-527656-tuscaloosa-alabama-united-states
Review of applications begins immediately and will continue until the positions are filled. Questions regarding the search should be directed to search chair Dr. Dr. Laurie Bonnici at lbonnici@ua.edu.
Requirements
Position Summary & Responsibilities:
This position provides an exciting opportunity for a passionate scholar and educator to teach in our growing Bachelor of Science in Informatics program and contribute to our online Master of Library and Information Studies (MLIS) curriculum. We are particularly interested in candidates are passionate about developing and teaching courses that address the technical and applied dimensions of informatics and information science, with emphasis on one or more of the following areas:
- Data Analytics and Visualization
- Web and Mobile System Design and Development
- Ethical and Responsible Data Science
- Applied Machine Learning and AI
