Sessional Lecturer: CHL5223H Applied Bayesian Methods
About the role
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.
Responsibilities
- Prepare course content and syllabus
- Prepare and give weekly 3‑hour lectures
- Hold scheduled office hours for at least one 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
Requirements
- 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
Pay
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.
Schedule
Class Schedule: Mondays, 2:00 p.m. to 5:00 p.m. (in person). Sessional dates: January 6 to April 9, 2027.