Sessional Lecturer: CHL5223H Applied Bayesian Methods

University of Toronto; Toronto; On-site; CAD 10,000 - 12,000; Part time

Job summary

University of Toronto is seeking a Sessional Lecturer II/III in Biostatistics to teach a Bayesian methods course in the Dalla Lana School of Public Health at St. George campus, Toronto. The lecturer will prepare content, deliver in-person lectures, and manage assessments.

The role requires a PhD in Biostatistics or Statistics, with advanced Bayesian knowledge and proven teaching capability. Sessions run January–April 2027, with salary per CUPE agreements and vacation pay where applicable.

Qualifications

• PhD in Biostatistics, Statistics, or a related field.
• Advanced comprehension of Bayesian methods, evidenced by research or teaching.
• Preference for candidates with excellent teaching in similar courses.

Responsibilities

• Prepare course content and syllabus; deliver weekly 3-hour lectures.
• Hold scheduled office hours for at least one hour weekly; respond to student questions.
• Provide regular feedback to students; grade and invigilate assessments; submit final grades.

Skills

Bayesian methods

Teaching experience

Education

PhD in Biostatistics/Statistics

Tools

R

Python

Stan

Job description

Date Posted: 09/28/2026
Req ID: 50349
Faculty/Division: Dalla Lana School of Public Health
Department: Dalla Lana School of Public Health
Campus: St. George (Downtown Toronto)
Existing Vacancy: Yes

Course Description & Learning Objectives: Bayesian methods are important tools for applied statisticians, biostatisticians, and data scientists. They provide a flexible framework for quantifying uncertainty and learning from new data. The course introduces the basics of Bayesian inference and Markov chain Monte Carlo methods, then shows students how to compute and make inferences for complex data problems. The learning objectives are to gain (i) an understanding of basic Bayesian inference, (ii) an understanding of the basic theory of Markov chain Monte Carlo methods, and (iii) proficiency in performing Bayesian data analysis on complex data problems.

Estimated course enrolment : 45

Estimated TA support : 70 hours

Class Schedule : Mondays, 2:00 p.m. to 5:00 p.m. (in person)

Sessional dates : January 6 to April 9, 2027

Salary : $9,997.48 (Sessional Lecturer I)

$10,699.22 (Sessional Lecturer I Long Term)

$10,699.22 (Sessional Lecturer II)

$10,953.96 (Sessional Lecturer II Long Term)

$10,953.96 (Sessional Lecturer III)

$11,228.90 (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, or a related field. Advanced comprehension of Bayesian methods, 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 course content and syllabus, 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, grade and invigilate course assessments; manage and submit the final course grades.

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.