Sessional Lecturer: MUI2000H - Special Topics in Urban Innovation - Unlocking AI for Cities: A Works
University of Toronto - 216 Jobs
Mississauga, ON
Job Details:
Date Posted: 11/17/2025
Req ID: 46042
Faculty/Division: UofT Mississauga
Department: UTM: Inst. for Management & Innovation
Campus: University of Toronto Mississauga (UTM)
Description:
Sessional Lecturer: MUI2000H – Special Topics in Urban Innovation – Unlocking AI for Cities: A Workshop on AI, Data, & Governance
The Institute for Management and Innovation's Master of Urban Innovation has the following Sessional Lecturer position available for the Winter 2026 term and invites applications from suitably qualified candidates who are not current University of Toronto students. No late applications can be considered.
Posting Date: November 17, 2025
Closing Date: November 21, 2025
Course Title: MUI2000H – Special Topics in Urban Innovation – Unlocking AI for Cities: A Workshop on AI, Data, & Governance
Class Schedule: Winter Term: Mondays 6PM – 8PM
Dates of Appointment: January 1st, 2026 – April 30th, 2026
Description of Course: This workshop will introduce the use of emerging AI applications in urban contexts. Students will develop the skills to interact with Gen AI tools and think critically about how urban stakeholders can most effectively and ethically engage with AI.
Position Description: All normal duties related to the design, teaching, and delivery of a university credit course, including weekly lectures, meeting with student teams to guide and advise them on their assignments, holding weekly office hours, marking and evaluating all student assignments, recording student attendance, recording and submitting grades, and communicating with students on course-related matters.
Additional responsibilities include building and maintaining the Quercus course shell; designing the course syllabus, content, and assignments in close consultation with the Program Director; liaising with the Program Director regarding course content and curriculum. Course to be co-taught by 2 instructors.
Minimum Qualifications:
A graduate degree in urban planning, public policy, data science, or a related field (or equivalent combination of education and professional experience).
Demonstrated expertise of urban systems and emerging technologies, including artificial intelligence, data governance, or smart city infrastructure.
A minimum of 5 years of professional experience in urban innovation, civic technology, or public sector.
Preferred Qualifications: Preference will be given to individuals with prior experience and demonstrated ability in teaching a similar course.
Salary (0.25 FCE course): Minimum $4910.35 as per CUPE 3902 Unit 3 Collective Agreement (includes 4% vacation pay).
Anticipated Enrollment: 20
Application Instructions: Submit your CV along with a completed application form (https://www.utm.utoronto.ca/imi/media/1025/download?inline) to: Simreet Aulakh, Program Coordinator, Master of Urban Innovation, at [email protected]. Include 'MUI2000H Sessional Lecturer Application' in the subject line. Kindly note only complete applications submitted by the deadline will be accepted, and only successful applicants will be contacted.
Closing Date: 11/21/2025, 11:59PM EDT
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This job is posted in accordance with the CUPE 3902 Unit 3 Collective Agreement.
It is understood that some announcements of vacancies are tentative, pending final course determinations and enrolment. Should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail.
Preference in hiring is given to qualified individuals advanced to the rank of Sessional Lecturer II or Sessional Lecturer III in accordance with Article 14:12 of the CUPE 3902 Unit 3 collective agreement.
Please note: Undergraduate or graduate students and postdoctoral fellows of the University of Toronto are covered by the CUPE 3902 Unit 1 collective agreement rather than the Unit 3 collective agreement, and should not apply for positions posted under the Unit 3 collective agreement.