Faculty/Department Faculty of Engineering and Applied Science Unit Engineering-Dean's Office Employee Group URFA CUPE 5791 Job Family N/A Category Sessional Number of Vacancies 1 Position Summary
This course explores the application of deep learning techniques in the field of Computer Vision, especially in the areas of object recognition, structured predictions and unsupervised deep learning. This course includes the fundamentals of computer vision such as image formation, feature detection, motion estimation, tracking, image classification and scene understanding.
Position Requirements
- A minimum of a Ph.D. in related discipline.
- Applicants will have related experience appropriate to the content in the course description as listed above.
- Previous teaching experience at a post secondary educational institution is an asset.
- Excellent written and oral communication skills and presentation skills.
- Submission of a one page statement of teaching philosophy as it relates to the course.
Physical Demands
n/a
Pay Grade Faculty Sessional Salary Range As per URFA Collective Agrement Status Term Work Hours
T 1730 2015
Course Modality In-person Course Dates and Time T 1730 2015 Duration (if Term/Temporary) September 1, 2026 - December 31, 2026 Full-Time/Part-Time Other Preference Posting No Preference Target Posting Not Targeted
Additional Information
Job Close Date
06/07/2026
Special Application Instructions
For existing Sessionals: You may upload new curriculum vitae and teaching dossier with your application or you may use previously uploaded documents. Existing sessional instructors do not have to provide new references and transcripts with each application. They may use previously uploaded documents.
If you require further information regarding this competition please contact the faculty you are applying to.
Applicants must be legally entitled to work in Canada, and the work associated with this position must be performed within Canada.
Diversity Statement
The University of Regina is committed to an inclusive workplace that reflects the richness of the community that we serve. The University welcomes applications from all qualified individuals, including individuals within the University’s employment equity categories of women, persons with disabilities, members of visible minorities, Indigenous persons, individuals of diverse gender and sexual orientation and all groups protected by the Human Rights Code.
