Posting Reason:

New Position

Location:

Main Campus

Session:

2026 Spring/Summer Semester | Trimestre printemps/été

Faculty:

Faculté de génie / Faculty of Engineering

Unit:

School of Engineering Design and Teaching Innovation_PT

Course Title:

Foundations of Machine Learning (online)

Course Code:

MIA 5100

Section:

Z

Course Description:

This course provides an in-depth exploration of the foundational topics in Machine Learning (ML) and Artificial Intelligence (AI), encompassing a broad range of concepts, algorithms, frameworks, methodologies, and practical applications. Topics will ranges from areas such as feature engineering, supervised and unsupervised learning, deep learning, natural language processing, and model deployment using state-of-art techniques. As part of this course, students are expected to develop the skills and knowledge necessary to design, implement, evaluate ML models, and deploy ML models effectively. Application areas will emphasize real-world contexts such as arts, business, social sciences, and law domains. The course format includes lectures, discussions, and lab sessions to facilitate comprehensive learning.

Posting limited to:

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

Date Posted (YYYY/MM/DD):

2026/01/26

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

2026/02/27

Expected Enrolment:

40

Approval date:

2026/01/26

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:

Mardi | Tuesday 19:00-22:00 - -

Requirements:

• Ph.D. in AI, Machine Learning, DTI, Computer Science, Statistics, Mathematics, Engineering, or a related field.
• Demonstrated expertise in AI/Machine Learning, with a general focus on areas such as Machine Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP), and model deployment, including applications in real-world scenarios related to law, business, social sciences, and arts.
• Teaching experience at the graduate and/or undergraduate level, preferably in AI/Machine Learning or related fields.
• Hands-on experience with industry tools and technologies commonly used for developing and deploying Machine Learning, Deep Learning, and NLP algorithms, such as Python, TensorFlow, PyTorch, Scikit-Learn, Transformers, NLTK, SpaCy, Streamlit, Flask, etc.

Preference will be given to candidates with experience in project-based learning or experiential learning approaches in AI/Machine Learning.

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.