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University of Toronto

Sessional Lecturer - STA380H5S LEC101 Computational Statistics

Mississauga, ON, Canada

Pay: Salary: Rate of pay for Sessional Lecturer I is $15,855.27 for a half course (inclusive of vacation pay).
Apply by: Oct 20, 2026
Positions available: 1
Posting language: English
Posted: Sep 28, 2026

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Original job description

Date Posted: 09/28/2026
Req ID: 50337
Faculty/Division: UofT Mississauga
Department: UTM: Math/Comp. Sciences
Campus: University of Toronto Mississauga (UTM)
Existing Vacancy: Yes

Description:

Course Number and Title: STA380H5S LEC101 Computational Statistics

Course Description: Computational methods play a central role in modern statistics and machine learning. This course aims to give an overview of some of the computational techniques that are useful in statistics. Topics include methods of generating random variables, Monte Carlo integration and variance reduction, Monte Carlo methods in inference, bootstrap and jackknife, resampling application, permutation tests, probability density estimation, and optimization.

Course Enrolment (Estimated): 100

Number of Positions: 1

TA Support: 50 hrs per 35 students enrolled per semester

Sessional Dates of Appointment: January 1, 2027 – April 30, 2027

Salary: Rate of pay for Sessional Lecturer I is $15,855.27 for a half course (inclusive of vacation pay).

Class Schedule: in person - Wed 11:00 -13:00

Minimum Qualifications: Ph.D. in Statistics, completed or nearly complete. Exceptional candidates with a Masters in Statistics will be considered. Advanced comprehension of the subject matter as evidenced by research activity and/or advanced teaching experience. Strong organizational, interpersonal, and communication skills.

Preferred Qualifications: Demonstrated evidence of excellence in teaching preferred. Preference will be given to candidates with demonstrable currency and mastery.

Description of Duties: Responsible for all aspects of delivering a university credit course including: planning the lectures, tutorials, assignments and tests and marking schemes; providing appropriate contact time outside of class to students, through office hours, emails, etc.; invigilating the final exam; managing the grades and submitting the final course grades; dealing with student petitions, setting and grading a make-up exam if required.

How to apply: If interested, please reply to: jobsmcs.utm@utoronto.ca with the following information: 1) CUPE 3902 (Unit 3) Employment Application Form: https://uoft.me/CUPE- 3902-Unit-3-Application-Form; 2) your current CV.

Candidates who are members of Indigenous, Black, racialized and 2SLGBTQ+ communities, persons with disabilities, and other equity-deserving groups are encouraged to apply, and their lived experience shall be taken into consideration as applicable to the position

Closing Date: 10/19/2026, 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.

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More about this job

Responsibilities

Plan and deliver lectures, tutorials, assignments, tests, and marking schemes for a university credit course, while providing student support through office hours and email. Invigilate the final exam, manage and submit grades, and handle student petitions and any required make-up exams.

Requirements

A completed or nearly completed Ph.D. in Statistics is required, though exceptional candidates with a master's degree in Statistics may be considered. Candidates should demonstrate advanced subject knowledge through research or advanced teaching experience, along with strong organizational, interpersonal, and communication skills; teaching excellence and current mastery of the subject are preferred.

Skills

Computational Statistics
Statistics
Machine Learning
Monte Carlo Methods
Statistical Inference
Bootstrap
Jackknife
Resampling
Permutation Tests
Probability Density Estimation
Optimization
Teaching
Course Planning
Grading
Communication
Organization

Visa sponsorship

Not detected in the job text

Keywords

Computational Statistics
Statistics
Machine Learning
Random Variable Generation
Monte Carlo Integration
Variance Reduction
Monte Carlo Methods
Statistical Inference
Bootstrap
Jackknife
Resampling
Permutation Tests
Probability Density Estimation
Optimization
University Teaching
Lectures
Tutorials
Assignments
Tests
Marking Schemes
Office Hours
Exam Invigilation
Grade Management
Student Petitions

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