Sessional Lecturer - KIN8247H: Artificial Intelligence in Sport Analytics
University of Toronto - Woodsworth College, Saint George, UT, United States
Sessional Lecturer - KIN8247H: Artificial Intelligence in Sport Analytics
Date Posted:
04/22/2026
Req ID:
47810
Faculty/Division:
Faculty of Kinesiology & Physical Education
Campus:
St. George (Downtown Toronto)
Existing Vacancy:
Yes
Course Details
Course Code:
KIN8247H: Artificial Intelligence in Sport Analytics
Course description:
This course provides a hands‑on exploration of artificial intelligence (AI) and machine learning (ML) through practical case studies. Students will analyze real‑world scenarios to understand how AI and ML are applied in a number of ways including, as a few examples, sport performance optimization, athlete services, injury prevention and rehabilitation, fan and community engagement, and business and financial decision making. This course emphasizes the use of cutting‑edge AI tools and data‑driven techniques applied in sport settings. By engaging with case studies and industry insights, this experience‑based approach ensures students gain firsthand knowledge of how AI is transforming sport analytics and prepares them for careers in this rapidly growing field.
Estimated TA support:
N/A
Estimated course enrolment:
30
Sessional dates of appointment:
September 1, 2026 – December 31, 2026
Appointment percentage:
0.5 FCE
Salary
Sessional Lecturer I – $9,997.48
Sessional Lecturer I – Long Term – $10,699.22
Sessional Lecturer II – $10,699.22
Sessional Lecturer II – Long Term – $10,953.96
Sessional Lecturer III – $10,953.96
Sessional Lecturer III – Long Term – $11,228.90
Minimum Qualifications
PhD preferred in a relevant area, or combination of relevant expertise and experience. Teaching experience in college or university setting is an asset.
Responsibilities
All duties associated with the design and teaching of a university credit course, including preparation and delivery of course content; development, administration, and marking of assignments, tests, and exams; calculation and submission of grades; holding regular office hours; supervising TAs assigned to the course, if applicable. This course will be taught in person.
Application Procedure
Please apply via: https://tracs.utoronto.ca/dept/kpe/app/sl
Closing Date:
05/13/2026, 11:59PM EDT
Other Information
The University of Toronto embraces diversity and is building a culture of belonging that increases our capacity to effectively address and serve the interests of our global community. We strongly encourage applications from Indigenous Peoples, Black and racialized persons, women, persons with disabilities, and people of diverse sexual and gender identities. We value applicants who have demonstrated a commitment to equity, diversity and inclusion and recognize that diverse perspectives, experiences, and expertise are essential to strengthening our academic mission. This positions is posted in accordance with the CUPE 3902 Unit 3 Collective Agreement.
The University is committed to the principles of the Accessibility for Ontarians with Disabilities Act (AODA). If you require any accommodations at any point during the application and hiring process, please contact uoft.careers@utoronto.ca.
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.
#J-18808-Ljbffr
Date Posted:
04/22/2026
Req ID:
47810
Faculty/Division:
Faculty of Kinesiology & Physical Education
Campus:
St. George (Downtown Toronto)
Existing Vacancy:
Yes
Course Details
Course Code:
KIN8247H: Artificial Intelligence in Sport Analytics
Course description:
This course provides a hands‑on exploration of artificial intelligence (AI) and machine learning (ML) through practical case studies. Students will analyze real‑world scenarios to understand how AI and ML are applied in a number of ways including, as a few examples, sport performance optimization, athlete services, injury prevention and rehabilitation, fan and community engagement, and business and financial decision making. This course emphasizes the use of cutting‑edge AI tools and data‑driven techniques applied in sport settings. By engaging with case studies and industry insights, this experience‑based approach ensures students gain firsthand knowledge of how AI is transforming sport analytics and prepares them for careers in this rapidly growing field.
Estimated TA support:
N/A
Estimated course enrolment:
30
Sessional dates of appointment:
September 1, 2026 – December 31, 2026
Appointment percentage:
0.5 FCE
Salary
Sessional Lecturer I – $9,997.48
Sessional Lecturer I – Long Term – $10,699.22
Sessional Lecturer II – $10,699.22
Sessional Lecturer II – Long Term – $10,953.96
Sessional Lecturer III – $10,953.96
Sessional Lecturer III – Long Term – $11,228.90
Minimum Qualifications
PhD preferred in a relevant area, or combination of relevant expertise and experience. Teaching experience in college or university setting is an asset.
Responsibilities
All duties associated with the design and teaching of a university credit course, including preparation and delivery of course content; development, administration, and marking of assignments, tests, and exams; calculation and submission of grades; holding regular office hours; supervising TAs assigned to the course, if applicable. This course will be taught in person.
Application Procedure
Please apply via: https://tracs.utoronto.ca/dept/kpe/app/sl
Closing Date:
05/13/2026, 11:59PM EDT
Other Information
The University of Toronto embraces diversity and is building a culture of belonging that increases our capacity to effectively address and serve the interests of our global community. We strongly encourage applications from Indigenous Peoples, Black and racialized persons, women, persons with disabilities, and people of diverse sexual and gender identities. We value applicants who have demonstrated a commitment to equity, diversity and inclusion and recognize that diverse perspectives, experiences, and expertise are essential to strengthening our academic mission. This positions is posted in accordance with the CUPE 3902 Unit 3 Collective Agreement.
The University is committed to the principles of the Accessibility for Ontarians with Disabilities Act (AODA). If you require any accommodations at any point during the application and hiring process, please contact uoft.careers@utoronto.ca.
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.
#J-18808-Ljbffr