Job Description
Location
Toronto, ON
Full job description
Date Posted: 07/15/2026
Req ID: 44290
Faculty/Division: Dalla Lana School of Public Health
Department: Dalla Lana School of Public Health
Campus: St. George (Downtown Toronto)
SESSIONAL LECTURER for Fall 2026 Term, at .5 FCE – CUPE 3902 Unit 3
Course# & Course Title: CHL5230 Applied Machine Learning for Health Data
Course Description & Learning Objectives: This course is intended to provide an introduction to Machine Learning and its applications in health and biomedical sciences. The methods will be illustrated within the context of data science; similarities and differences with traditional statistical methods will be discussed. Selected theoretical background will be presented with emphasis on hands-on practical applications of prediction, classification and clustering, using simulated and real health data. The learning objectives are to gain (i) an understanding of machine learning and data science, as well as principles of applying different approaches for the tasks at hand, (ii) practical experience performing data analysis using machine learning methods and interpreting results, and (iii) sufficient knowledge for determining appropriate methodology for real applications in health services research and other applied health sciences.
Estimated course enrolment: 45
Estimated TA support: 50 hours
Class Schedule: Mondays, 2pm-5pm
Sessional dates: September to December 2026
Salary: $9,820.70 (Sessional Lecturer I)
$10,510.04 (Sessional Lecturer I Long Term)
$10,510.04 (Sessional Lecturer II)
$10,760.28 (Sessional Lecturer II Long Term)
$10,760.28 (Sessional Lecturer III)
$11,030.36 (Sessional Lecturer III Long Term)
(Salary inclusive of 4% or 6% vacation pay, where applicable)
Please note that should rates stipulated in the Collective Agreement vary from rates stated in this posting, the rates stated in the Collective Agreement shall prevail.
Minimum Qualifications: PhD in Biostatistics, Statistics, Computer Science or related field. Advanced comprehension of the subject matter as evidenced by research activity and/or advanced teaching experience. Preference will be given to candidates with demonstrated experience of excellence in teaching a similar course.
Description of duties: Prepare and give weekly 3-hour lectures; hold scheduled office hours for at least an hour per week; respond to student questions regarding the course and course materials; provide regular feedback to students; prepare, invigilate, and grade exams; manage and submit the final course grades.
Application Process:
All individuals interested in this position must submit, via email, a Curriculum Vitae, and the CUPE 3902 Unit 3 application form (PDF or RTF, also available at https://uoft.me/CUPE-3902-Unit-3-Application-Form), to:
c/o Christine Lowe
Dalla Lana School of Public Health
University of Toronto
Email: christine.lowe@utoronto.ca
Closing Date: 07/22/2026, 11:59PM EDT
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.
Toronto, ON
Full job description
Date Posted: 07/15/2026
Req ID: 44290
Faculty/Division: Dalla Lana School of Public Health
Department: Dalla Lana School of Public Health
Campus: St. George (Downtown Toronto)
SESSIONAL LECTURER for Fall 2026 Term, at .5 FCE – CUPE 3902 Unit 3
Course# & Course Title: CHL5230 Applied Machine Learning for Health Data
Course Description & Learning Objectives: This course is intended to provide an introduction to Machine Learning and its applications in health and biomedical sciences. The methods will be illustrated within the context of data science; similarities and differences with traditional statistical methods will be discussed. Selected theoretical background will be presented with emphasis on hands-on practical applications of prediction, classification and clustering, using simulated and real health data. The learning objectives are to gain (i) an understanding of machine learning and data science, as well as principles of applying different approaches for the tasks at hand, (ii) practical experience performing data analysis using machine learning methods and interpreting results, and (iii) sufficient knowledge for determining appropriate methodology for real applications in health services research and other applied health sciences.
Estimated course enrolment: 45
Estimated TA support: 50 hours
Class Schedule: Mondays, 2pm-5pm
Sessional dates: September to December 2026
Salary: $9,820.70 (Sessional Lecturer I)
$10,510.04 (Sessional Lecturer I Long Term)
$10,510.04 (Sessional Lecturer II)
$10,760.28 (Sessional Lecturer II Long Term)
$10,760.28 (Sessional Lecturer III)
$11,030.36 (Sessional Lecturer III Long Term)
(Salary inclusive of 4% or 6% vacation pay, where applicable)
Please note that should rates stipulated in the Collective Agreement vary from rates stated in this posting, the rates stated in the Collective Agreement shall prevail.
Minimum Qualifications: PhD in Biostatistics, Statistics, Computer Science or related field. Advanced comprehension of the subject matter as evidenced by research activity and/or advanced teaching experience. Preference will be given to candidates with demonstrated experience of excellence in teaching a similar course.
Description of duties: Prepare and give weekly 3-hour lectures; hold scheduled office hours for at least an hour per week; respond to student questions regarding the course and course materials; provide regular feedback to students; prepare, invigilate, and grade exams; manage and submit the final course grades.
Application Process:
All individuals interested in this position must submit, via email, a Curriculum Vitae, and the CUPE 3902 Unit 3 application form (PDF or RTF, also available at https://uoft.me/CUPE-3902-Unit-3-Application-Form), to:
c/o Christine Lowe
Dalla Lana School of Public Health
University of Toronto
Email: christine.lowe@utoronto.ca
Closing Date: 07/22/2026, 11:59PM EDT
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.
💡 Quick Summary
Seeking a career-building opportunity? The Sessional Lecturer: CHL5230H Applied Machine Learning for Health Data position is now open for candidates interested in the Education Jobs sector. This role in Toronto offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in Education Jobs is a plus.