Computational Intelligence

University of Guelph

Guelph

On-site

CAD 25,000 - 39,000

Part time

12 hours ago
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Job summary

University of Guelph is seeking Teaching Assistants, Graduates Service Assistants, and Sessional Lecturers for Winter 2027 in the School of Computer Science. Positions are in-person with responsibilities including labs, grading, and student consultations.

Applicants must be enrolled for Winter 2027 and capable of conducting labs and tutoring with strong AI knowledge. The posting notes guidance on AI usage and requires availability for the full term ending May 7, 2027.

Qualifications

  • Must be enrolled as a student in Winter 2027 and able to complete in-person responsibilities.
  • Must have excellent writing skills and be able to effectively critique (in writing) student assignments.
  • The ability to communicate computer science concepts to students effectively, both in written form and verbally.
  • Must be available to support scheduled lab hours throughout the entire semester.
  • Strong knowledge on artificial intelligence, machine learning, and intelligent systems.
  • Strong Python and/or Java programming skills.
  • Ability to grade written assignments according to a specified rubric.
  • Strong organizational and time management skills.

Responsibilities

  • Orientation-Training
  • Office Hours
  • Preparation
  • Student Consultation
  • Conducting Labs/Seminars
  • Meetings
  • Invigilating Exams
  • Grading

Skills

AI knowledge
Python
Java
Excellent writing skills
Time management

Job description

for Teaching Assistants, Graduates Service Assistants & Sessional Lecturers
Teaching Assistant Work Assignment
Type of Work Assignment and Bargaining Unit:

GTA, Unit 1

Academic Unit:

School of Computer Science

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:

Yes

Course Number:

CIS*4780

Course Name:
Course Format:

In-Person

Course Description:

See Course Calendar

Other Course Description or Assignment Information:

The total number of assignments is a low estimate of the number of available appointments. The final number may be higher and it will depend on class enrolment at the time of assignment.

60

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.

Number of Available 0.25 (35 hour) Work Assignments:

0

Number of Available 0.5 (70 hour) Work Assignments:

0

Number of Available 0.75 (105 hour) Work Assignments:

0

Number of Available Full 1.0 (140 hour) Work Assignments:

1

Total Number (Load) of Assignment(s) Available:

1

Duties and Responsibilities
Anticipated Duties and Responsibilities:
  • Orientation-Training
  • Office Hours
  • Preparation
  • Student Consultation
  • Conducting Labs/Seminars
  • Meetings
  • Invigilating Exams
  • Grading
Other Duties Described:

NOTICE: The use of AI to perform job responsibilities must be directed by or approved beforehand in writing by the supervisor or course instructor.

Qualifications
Required Qualifications:
  • Must be enrolled as a student in Winter 2027 and able to complete in-person responsibilities.
  • Must have excellent writing skills and be able to effectively critique (in writing) student assignments.
  • The ability to communicate computer science concepts to students effectively, both in written form and verbally.
  • Must be available to support scheduled lab hours throughout the entire semester.
  • Strong knowledge on artificial intelligence, machine learning, and intelligent systems.
  • Strong Python and/or Java programming skills.
  • Ability to grade written assignments according to a specified rubric.
  • Strong organizational and time management skills.
Preferred Qualifications:
  • Demonstrated knowledge of neural networks.
  • Demonstrated knowledge on probabilistic inference ideally with Bayesian networks and some inference methods.
  • Demonstrated knowledge of evolutionary computation (genetic algorithms, swarm intelligence).
  • Previous successful TA experience supporting AI-related courses.
Days Required and Wages
Days and Times Required:

See duties and qualifications above, as well as WebAdvisor for lecture and lab days and times. Must be available to complete TA duties as required until the end of the work period (May 7th, 2027).

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):
Other Posting Information
Application Deadline (All postings will automatically expire at 11:59 pm on this day):

Thursday, November 12, 2026

Posting Email Contact:
Hiring Contact Information:

Christine Harris, Instructional Support Coordinator - teachingsupport@socs.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.

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