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University of Guelph in Ontario offers Winter 2027 teaching assignments for STAT*2120: Probability and Statistics for Engineers. The postings cover Teaching Assistant, Graduate Service Assistant, and Sessional Lecturer roles with in-person duties and university hours (8:30 AM–10:00 PM EST) as required.
Applicants must be enrolled in a graduate program in Mathematics and Statistics and demonstrate strong statistics and math knowledge.
for Teaching Assistants, Graduates Service Assistants & Sessional Lecturers
Teaching Assistant Work Assignment
Type of Work Assignment and Bargaining Unit:
GTA, Unit 1
Academic Unit:
Mathematics and Statistics
Semester(s) of Assignment(s):
Winter 2027
This Work Assignment(s) may be assigned to fulfill the terms and conditions of a guaranteed Job Security Period:
Some
Course Number:
STAT*2120
Course Name:
Probability and Statistics for Engineers
Course Format:
In-Person
Course Description:
See Course Calendar
Other Course Description or Assignment Information:
When applying for this work assignment, please be aware that this course is an “in‑person” format. Candidate will be required to work during regular University instructional hours (8:30 AM to 10:00 PM EST) as required. Some of the duties of this job may include conversing with students via chat or other interactive online software, for which the candidate must have access to a computer and a reliable internet connection or in‑person office hours. In addition, the candidate may be required to perform duties during scheduled face‑to‑face lecture and/or lab times, course evaluations, and invigilation of midterms and finals. (check WebAdvisor for course schedule).
150
Level of Appointment for GTA, GSA-1 UTA(s)
The following is an estimate of number and levels of available appointments at the time of posting. This may change (increase or decrease) at the time of assignment contingent on departmental needs.
This Posting includes Multiple Work Assignments:
Yes
Number of Available 0.25 (35 hour) Work Assignments:
0
Number of Available 0.5 (70 hour) Work Assignments:
2
Number of Available 0.75 (105 hour) Work Assignments:
0
Number of Available Full 1.0 (140 hour) Work Assignments:
0
Total Number (Load) of Assignment(s) Available:
2
Other Duties Described:
Enrollment in a graduate program in Mathematics and Statistics or closely related program. A thorough understanding of introductory statistics as well as a strong mathematical background is required.
Excellent communications skills plus familiarity with CourseLink would be an asset.
Days and Times Required:
See WebAdvisor for course schedule.
Period of the Work Assignment (Start Date and End Date):
January 7, 2027 to May 7, 2027
Wage Rate (per hour, per semester, per Full-load 1.0 Work Assignment):
Application Deadline (All postings will automatically expire at 11:59 pm on this day):
Monday, November 2, 2026
Posting Email Contact:
Hiring Contact Information:
Tricia Townsend, Graduate Program Specialist, Department of Mathematics and Statistics, gradms@uoguelph.ca
At the University of Guelph, fostering a culture of inclusion is an institutional imperative. The University invites and encourages applications from all qualified individuals, including from groups that are traditionally underrepresented in employment, who may contribute to further diversification of our Institution. For more information, the Office of Diversity and Human Rights (DHR) is a welcoming, safe and confidential one‑stop shop for information, training and support on issues relating to diversity and human rights on our campus. All applicants must be eligible to work in Canada specifically at the University of Guelph before applying for an academic work assignment. All successful applicants must perform their work in Ontario and must be able to attend on‑campus in‑person meetings as required. Teaching Assistant work assignments are unionized with CUPE 3913 and their terms and conditions of work are covered by the Unit 1 Collective Agreement between the University and CUPE 3913 (email contact: president@cupe3913.on.ca). The use of AI to perform job responsibilities must be directed by or approved beforehand in writing by the supervisor or course instructor.