Assistant Clinical Professor of Computer Science
- Full Time
The University of Chicago: Physical Sciences Division: Department of Computer Science
The Department of Computer Science at the University of Chicago invites applications for a position of Assistant Clinical Professor of Computer Science to teach Databases classes in its Masters Program in Computer Science (MPCS) and in its joint program with the Harris School of Public Policy, the Master of Science in Computational Analysis & Public Policy (CAPP).
This full-time, benefit-eligible appointment is for an initial three-year term, with possibility of renewal. This is a teaching position with no research responsibilities, and a teaching load of six courses across three academic quarters of the year (Autumn, Winter, Spring).
The person holding this position will teach at least two different courses: MPCS 53001 Databases and CAPP 30235 Databases for Public Policy. Syllabuses for the latest offerings of these classes can be found at https://mpcs-courses.cs.uchicago.edu/2019-20/spring/courses/53001 and https://www.classes.cs.uchicago.edu/archive/2019/spring/30235-1/syllabus.html. Depending on the applicant’s background and interests, the person holding this position may also be asked to teach classes covering advanced topics in Databases.
- A doctorate in Computer Science or a related field at the time of appointment, or 10 years of relevant industry experience.
- Teaching experience in Computer Science or a related field at the undergraduate or graduate level, as either an instructor of record or a teaching assistant.
- Work experience in a computing-related industry.
- Applications must be submitted online through the University of Chicago Jobs website: apply.interfolio.com/77082. Review of applications will begin on August 20, 2020 and continue until the position is filled. The following materials are required:
- a curriculum vitae, a one to three-page teaching statement, a list of three references contact information.
Optional materials may be submitted:
- teaching evaluations from past teaching at the university level.