Sessional Lecturer - Deep Learning - Fall 2026

Tracking Code: req2177

Faculty of Engineering and Applied Science

Number of Sections: 1

Appointment Type: Limited Term

Salary Grade: Administered in accordance with Appendix A of the Sessional Lecturers Collective Agreement with PSAC

Posting Date: August 5, 2026

Closing Date: August 19, 2026 (11:59 pm EST)

Course Name and Number: ENGR 4170U - Deep Learning

Course Start Date: September 8th, 2026

Course End Date: December 23rd, 2026

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

Course Description: ENGR 4170U, Deep Learning

This course provides an in-depth introduction to the fundamental concepts of deep learning and their applications in engineering. Topics include review of neural networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transfer Learning, and Reinforcement Learning. The course will also cover the latest techniques and tools for designing, implementing, training, testing, and evaluating the performance of deep learning models.

In addition, the course will cover the ethical implications of deep learning techniques in engineering and society, as well as ways to avoid bias in deep learning models.

Responsibilities/Accountabilities:

The primary responsibility of the applicant is to (prepare and) teach a theoretically, methodologically sophisticated and relevant university course at the undergraduate 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. The collective agreement may be found on the Human Resources section of our website. 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 undergraduate 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 undergraduate students in an Mechanical and Manufacturing 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 undergraduate 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 Mechanical Engineering or Electrical or Computer Engineering, or equivalent
• Documented experience in the topic areas covered by the course outline is 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 August 19, 2026 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.

The university acknowledges the lands and people of the Mississaugas of Scugog Island First Nation which is covered under the Williams Treaties. We are situated on the Traditional Territory of the Mississaugas, a branch of the greater Anishinaabeg Nation which includes Algonquin, Ojibway, Odawa and Pottawatomi.

Job Location: Oshawa, ON, Canada