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Sessional Lecturer- MHI2002H Emergent Topics in Health Informatics

CUPE Local 3902 (Unit 3) Job Posting

Posting Date: September 21, 2026

Program: Masters of Health Informatics (MHI)

Sessional Dates of Appointment: Winter 2027, January- AprilExisting Vacancy: Yes

Course Title: MHI2002H Emergent Topics in Health Informatics: Intelligent Medicine, Machine Learning and Knowledge Representation

Course Description:

This course is designed for students to understand the issues associated with the use of data management technology and analytics solutions in the healthcare system. This includes systems and technologies used to generate, harvest and store clinical data and methods used to create predictive models (including but not limited to methods associated with machine learning). Furthermore, issues related to delivery of predictive analytics and implementation of algorithms in care settings along with clinical, business and ethical challenges will be explored.

In addition, an overview of the issues within the health industry that are driving the use of data, will be reviewed, including population health management, clinical decision support, and advanced research. The goal is for students to be able to gain experience in the description, architecture and implementation planning of data infrastructure in the healthcare system along with providing a strong foundation in regard to analytics lifecycle and methods.

Objectives:

Students will enhance abilities to:

Describe and conceptualize data infrastructure used in the healthcare system, including classical and non-classical sources of data and the technologies and methods used to harvest and store clinical data.

Utilize statistical and machine learning tools to create and validate predictive models and present analytics results using visualization tools.

Identify and problem-solve the organizational, clinical and ethical implementation challenges associated with predictive algorithms in healthcare.





Gain the ability to position data management and advanced analytics in the context of health system challenges and business models.

Class schedule: Weekly sessions from 1:10pm–4pm on Fridays, between Jan 15 and March 12, 2027.

Delivery mode: In-person

Qualifications:

- A PhD or Masters level education with recent experience in clinical and health informatics, preferably in the areas of AI, ML, design, modeling, implementation and policy ;
- A robust understanding of clinical/clinician work processes, as influenced by health informatics and AI and ML technologies.
- P ast teaching experience related to health informatics, preferably at the graduate level;
- Comfortable with electronic teaching tools such as Learning Management Systems (e.g., Blackboard), PowerPoint, as well as on-line collaboration tools (Blogs, Wikkis , Discussion Boards, Webinars, or Video-conferencing ).

Duties:

Course co- instructor for a skilled graduate course using competency-based learning and assessment methods.

Responsible for course design and assessment of student outcomes. Must be accessible to students outside of classroom hours.

Please note that should rates stipulated in the collective agreement vary from ratesstatedin thisposting,the ratesstatedin the collective agreement shall prevail.

Closing Date: October 12, 2026

This job is postedin accordance withthe 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.





Preferenceinhiringisgiventoqualifiedindividualsadvancedto therankofSessionalLecturerII orSessionalLecturer IIIinaccordancewithArticle14:12oftheCUPE3902Unit3collectiveagreement.

Pleasenote:UndergraduateorgraduatestudentsandpostdoctoralfellowsoftheUniversityofTorontoarecoveredby theCUPE3902Unit1collectiveagreementrather thantheUnit3collectiveagreement,andshouldnotapplyforpositionspostedundertheUnit3collectiveagreement.

Diversity Statement The University of Toronto embraces Diversity and is building aculture of belonging that increases our capacity to effectivelyaddress and serve the interests of our global community. Westrongly encourage applications from Indigenous Peoples,Black and racialized persons, women, persons withdisabilities, and people of diverse sexual and gender identities.We value applicants who have demonstrated a commitment toequity, diversity and inclusion and recognize that diverseperspectives, experiences, and expertise are essential tostrengthening 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 [email protected] .

📌 Sessional Lecturer-H Emergent Topics in Health Informatics (Toronto)
🏢 University of Toronto
📍 Toronto