Sessional Lecturer - ENGR 5010G - Advanced Optimization - Summer 2025

Sessional Lecturer - ENGR 5010G - Advanced Optimization - Summer 2025

Tracking Code: req1533

Faculty of Engineering and Applied Science

Number of Sections: 1

Appointment Type: Limited Term

Salary Grade: Administered in accordance with the Collective Agreement

Posting Date: March 27, 2025

Closing Date: April 22, 2025 (7:00 pm EST)

Course Name and Number: ENGR 5010G - Advanced Optimization

Course Start Date: June 23, 2025

Course End Date: August 15, 2025

Reporting to the Dean, Faculty of Engineering and Applied Science, the Sessional Lecturer will provide instruction to graduate students.

Course Description: The objective of this course is to understand the principles of optimization and its application to engineering problems. Topics covered include the steepest descent and Newton methods for unconstrained optimization; golden section, quadratic, cubic and inexact line searches; conjugate and quasi-Newton methods; the Fletcher-Reeves algorithm; fundamentals of constrained optimization theory; simplex methods for linear programming; modern interior-point methods; active-set methods and primal-dual interior point methods for quadratic and convex programming; semi-definite programming algorithms; sequential quadratic programming; and interior-point methods for non-convex optimization. In addition, implementation issues and current software packages/algorithms for optimization will be covered. Global optimization, including genetic algorithms and simulated annealing, will be introduced.

Responsibilities/Accountabilities: The primary responsibility of the applicant is to (prepare and) teach a theoretically, methodologically sophisticated and relevant university course at the graduate level within the parameters of the course description listed above. In keeping with a technology-enhanced learning environment utilizing laptop computers and wireless connectivity, the Sessional Lecturer will deliver the course using a technology-enhanced learning approach that includes a variety of inclusive teaching and learning strategies and assessments which may be delivered in a hybrid format. The Sessional Lecturer will prepare, deliver and grade all course material and assignments and be available for consultation with students during and following completion of the course session. Learning objectives and outcomes are expected to be described in a course outline/syllabus. Timely marking of assessed assignments and marking rubric is expected.

This position falls within the bargaining unit represented by the Public Service Alliance of Canada (PSAC) and will be subject to the terms and conditions of the collective agreement between the University and PSAC. Candidates will be required to certify that they are currently legally eligible to work in Canada for the duration of the contract.

Teaching Qualifications: The successful candidate should have a demonstrated strong potential for excellent teaching contributions while showing an aptitude for teaching at the University graduate level.

Required Skills:

• A proven commitment to providing innovative and inclusive teaching strategies.
• Evidence of an ability to work in a collaborative environment.
• Excellent communication skills with an ability to maintain effective relationships with a range of faculty, staff and students.
• Preferred candidates will have demonstrated excellence in teaching graduate students in a Software Engineering program.
• Demonstrated knowledge and experience with a Learning Management System such as Canvas.
• Demonstrated ability to deliver classes remotely, using online modalities such as Kaltura Media and Google Meets and/or Zoom.
• Proven experience managing graduate classes and diverse populations.
• Excellent organizational skills are needed to effectively manage course delivery, the marking of assignments and exams, and the ability to respond to student enquiries in a timely manner.

Education & Qualifications:

• A PhD in Software Engineering, or equivalent of education and extensive software industrial experience.
• Documented software industrial experience in the topic areas covered by the course outline are needed.

Priority will be given to an Internal Applicant who has taught any course for the University in three (3) different sessions in the past thirty-six (36) months, provided they meet the requirements for the position as outlined in the job posting.

How to Apply: Interested candidates should submit in electronic format a covering letter and their resume. Applications will be accepted until April 22, 2025 or until a suitable candidate is found. We appreciate all applications received; however, only those candidates selected for an interview will be contacted.

Ontario Tech University is actively committed to equity, diversity, inclusion, indigenization and decolonization (EDIID), and welcomes applications from all qualified candidates, while especially encouraging applications from First Nations, Metis, Inuit peoples, Indigenous peoples of North America, racialized persons, persons with disabilities, and those who identify as women and/or 2SLGBTQ+. All qualified candidates are encouraged to apply; however, Canadian citizens, permanent residents, Indigenous Peoples in Canada, and those eligible to work in Canada, will be given priority.

Ontario Tech University respects people's different needs and therefore will take all reasonable steps to ensure accommodation for applicants where appropriate. The University is also committed to ensuring that confidentiality is maintained throughout all aspects of the recruitment cycle.

If you require accommodation, please contact Julie Day, Health and Disability Management Specialist. For more information about the universities policies for accommodating employees with disabilities please review the university’s Accessibility Policy.

Job Location: Oshawa, ON, Canada