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Sessional Lecturer: CHL5230H Applied Machine Learning for Health Data

Date Posted: 07/15/2025
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 2025 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 2025                   

 

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: 07/22/2025, 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.

 

Diversity Statement

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.

As part of your application, you will be asked to complete a brief Diversity Survey. This survey is voluntary. Any information directly related to you is confidential and cannot be accessed by search committees or human resources staff. Results will be aggregated for institutional planning purposes. For more information, please see http://uoft.me/UP.

Accessibility Statement

The University strives to be an equitable and inclusive community, and proactively seeks to increase diversity among its community members. Our values regarding equity and diversity are linked with our unwavering commitment to excellence in the pursuit of our academic mission.

The University is committed to the principles of the Accessibility for Ontarians with Disabilities Act (AODA). As such, we strive to make our recruitment, assessment and selection processes as accessible as possible and provide accommodations as required for applicants with disabilities.

If you require any accommodations at any point during the application and hiring process, please contact uoft.careers@utoronto.ca.


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