Lecturer

Sessional Lecturer - MMF2021H1F: Numerical Methods for Finance (Section LEC 0101) Salary (per Section) $4,998.74 Sessional Lecturer I $5,349.61 Sessional Lecturer I - Long Term $5,349.61 Sessional Lecturer II $5,476.98 Sessional Lecturer II - Long Term $5,476.98 Sessional Lecturer III $5,614.45 Sessional Lecturer III - Long Term Date Posted: 07/21/2026 Req ID: 49472 Faculty/Division: Faculty of Arts & Science Department: Dept of Economics Campus: St. George (Downtown Toronto) Existing Vacancy: Yes Course Number and Title MMF2021H1F: Numerical Methods for Finance (Section LEC 0101) Course Description This course provides a rigorous introduction to numerical methods essential for modern quantitative finance. Students will master key techniques, including finite difference methods for solving partial differential equations arising in option pricing, Monte Carlo simulation and variance reduction techniques for pricing complex derivatives and risk measurement, numerical optimization algorithms for portfolio optimization and calibration of financial models. We will focus on practical applications to real-world problems in derivatives pricing, risk management, and algorithmic trading. Through theoretical lectures, coding assignments, and case studies, students will develop the ability to select, implement, and validate appropriate numerical methods for quantitative finance challenges. Estimated course enrolment: 30 Estimated TA support: n/a Class Schedule: Wednesday 5:00-8:00 pm The delivery method for this course is in-person. Sessional dates of appointment: September 9 – November 4, 2026 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 Advanced degree in Mathematical Finance Industry experience in partial differential equations, pricing options and complex derivatives Prior experience teaching this course (or a similar course) at the university level Ability and experience teaching large classes Preferred Qualifications Industry experience in numerical optimization algorithms and portfolio optimization Description Of Duties Preparation and delivery of lectures in this course Preparation, supervision and grading of tests and examinations in accordance with university regulations Providing scheduled office hours for academic counseling of students Closing Date: 08/14/2026, 11:59PM EDT Diversity Statement The University of Toronto embraces Diversity and is building a culture of belonging that increases our capacity to effectively address and serve the interests of our global community. We strongly encourage applications from Indigenous Peoples, Black and racialized persons, women, persons with disabilities, and people of diverse sexual and gender identities. We value applicants who have demonstrated a commitment to equity, diversity and inclusion and recognize that diverse perspectives, experiences, and expertise are essential to strengthening 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 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 View email address on click.appcast.io. Job Segment: Developer, Financial, Risk Management, Technology, Finance 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. #J-18808-Ljbffr