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Course position:
LMP1210H: Basic principles of machine learning in biomedical research (0.5 FCE)
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Course description:
This course is intended for graduate students in Health Sciences to learn the basic principles of machine learning in biomedical research and to build and strengthen their computational skills of medical research. The course aims to equip students with the fundamental knowledge of machine learning (ML).
During the course, students will acquire basic computational skills and hands-on experience to deploy ML algorithms using python. Students are expected to be familiar in python prior to taking this course.
Students will learn the current practices and applications of ML in medicine and understand what ML can and cannot do for medicine. The goal of this course to establish an essential foundation for graduate students to take the first steps in computational research in medicine.
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# of Available Position and FCE:
1 position x 0.25 FCE (co-teaching course)
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Dates of Appointment
Winter 2027 semester
January 1, 2027 – April 30, 2027
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Class schedule:
Wednesdays from 11am-1pm
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Estimated course enrollment:
~30 students
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Estimated TA support:
n/a
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Salary (0.25 FCE - course will be co-taught):
Sessional Lecturer I - $4,998.74
Sessional Lecturer I - Long Term - $5,349.61
Sessional Lecturer II - $5,349.61
Sessional Lecturer II - Long Term - $5,476.98
Sessional Lecturer III - $5,476.98
Sessional Lecturer III - Long Term -$5,614.45
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.
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Minimum Qualifications:
PhD in computer science or in a health science discipline with a strong background in machine learning and its application in biomedical research. Programming skills in Python are required for the assignments. Also required is the demonstration of leadership in curating technical material to present to an audience as demonstrated by the organization of research workshops.
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Preferred Qualifications:
Extensive research experience with published research papers applying ML in biomedical research is strongly preferred. Experience creating and maintaining a website that highlights the course requirements, announcements and grade breakdown.
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Duties:
Preparation and delivery of course content, including lectures and/or seminars; creation of courseware; the development and administration of assignments, tests and examinations; marking and grading of student work and the submission of grades to appropriate University officials, maintenance of reasonable hours of student contact outside of scheduled contact hours. Creation, maintenance and management of the Quercus site for the course, collecting material from the lecturers in advance and posting lecturer slide sets (power points), handouts, reading lists, journal articles and bibliographies and preparing other course content as required.
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APPLICATION DEADLINE is November 13, 2026
Please be advised that only selected applicants will be invited for an interview. However, all applicants will be notified of the outcome of their application.
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POLICIES:
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.
All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority.
Diversity Statement
The University of Toronto is strongly committed to diversity within its community and especially welcomes applications from racialized persons / persons of colour, women, Indigenous / Aboriginal People of North America, persons with disabilities, LGBTQ2S+ persons, and others who may contribute to the further diversification of ideas.
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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INQUIRIES:
If you have any questions, contact lmp.hr@utoronto.ca.
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A special link to resume the form will be sent to your email address.
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