APTPUO-Fall 2026-MIA5130 (online)

Posting Reason:

New Position

Location:

Main Campus

Academic Period:

2026 Fall Semester

Faculty:

Faculté de génie / Faculty of Engineering

Academic Unit:

École de conception et d’innovation pédagogique en génie \\ School of Engineering Design and Teaching Innovation

Course Title:

SYSTEMS OPTI AND MNGT (online)

Course Code:

MIA5130

Section:

A

Course Description:

This course covers the analysis of user requirements, the design of optimization models, and the implementation of solutions to improve efficiency and performance. It introduces the key concepts of systems thinking, emphasizing its application to both economic and hierarchical systems, and highlights the application of simulation, modeling, optimization, and management to economic systems.

Posting limited to:

Professeur à temps-partiel régulier / Regular Part-Time Professor

Date Posted (YYYY/MM/DD):

2026/07/06

Applications must be received BEFORE (YYYY/MM/DD):

2026/08/07

Expected Enrolment:

40

Approval date:

2026/07/06

Number of credits:

3

Work Hours:

39

Hourly Rate:

Enseignement / Teaching: $239.47 (2024-2025)

The academic year starts on September 1 and ends on August 31.

These rates do not included vacation pay nor statutory pay.

These rates will be applied until a new collective agreement is ratified. Retro will be paid after the ratification.

Course type:

B

Posting type:

Régulier / Regular

Language of instruction:

Anglais | English

Competence in second language:

Active

Course Schedule:

Mercredi | Wednesday 19:00-22:00 - -

Requirements:

• Ph.D. in computer science, engineering, mathematics, operations research, or a closely related field with strong focus on AI/ML is required.
• Experience teaching a similar course at the graduate level and/or undergraduate level
• Experience working on projects involving optimization techniques, such as developing algorithms, modelling complex systems, or improving business processes, with applications in AI, machine learning, or data-driven decision systems. 
• Experience working with optimization tools and computational methods, such as Python, MATLAB, and/or optimization libraries/solvers
• Familiarity with AI or data-driven approaches in optimization, such as machine learning-enhanced optimization, heuristic methods, and intelligent decision systems.
• Demonstrated industry experience applying AI, optimization, or data-driven methods to real-world problems including areas such as system optimization, data analysis, or process improvement, is highly desirable 

Additional Information and/or Comments:

An acceptable level of education and/or experience could be viewed as being equivalent to the educational required and/or demonstrated experience. If you are invited to continue the selection process, please notify us of any adaptive measures you might require. Information you send us will be handled respectfully and in complete confidence. Employees are required under provincial law to successfully complete all mandatory legislated training. The list of training may be modified by provincial law.

The hiring process will be governed by the current APTPUO collective agreements; you can click here for the main unit, here for the OLBI unit, or here for the Toronto/Windsor unit to find out more.

The University of Ottawa embraces diversity and inclusion in the workplace. We are passionate about our people and committed to employment equity. We foster a culture of respect, teamwork and inclusion, where collaboration, innovation, and creativity fuel our quest for research and teaching excellence. While all qualified persons are invited to apply, we welcome applications from qualified Indigenous persons, racialized persons, persons with disabilities, women and LGBTQIA2S+ persons. The University is committed to creating and maintaining an accessible, barrier-free work environment. The University is also committed to working with applicants with disabilities requesting accommodation during the recruitment, assessment and selection processes. Applicants with disabilities may contact [email protected] to communicate the accommodation need. All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority.

Prior to May 1, 2022, the University required all students, faculty, staff, and visitors (including contractors) to be fully vaccinated against Covid-19 as defined in Policy 129 – Covid-19 Vaccination. This policy was suspended effective May 1, 2022 but may be reinstated at any point in the future depending on public health guidelines and the recommendations of experts.