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:

Generative AI and Large Language Models

Course Code:

MIA5150

Section:

A

Course Description:

Foundations and practices of Generative AI and Large Language Models (LLMs), covering the full lifecycle from model development to deployment. Explore generative model families, including Transformers, autoregressive models, diffusion models, GANs, and VAEs, and their multimodal applications across text, image, audio, and video. Modern techniques such as fine-tuning, parameter-efficient training, in-context learning, contrastive learning, retrieval-augmented generation (RAG), and multi-agent systems for enabling complex reasoning, coordination, and autonomous task execution are discussed. Key practices in prompt engineering, inference optimization, safety and alignment, and responsible AI deployment. Practical applications across diverse domains.

Prerequisite: MIA5100, MIA5126 or equivalent.

Posting limited to:

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

Date Posted (YYYY/MM/DD):

2026/05/26

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

2026/06/27

Expected Enrolment:

30

Approval date:

2026/05/26

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:

Vendredi | Friday 19:00-22:00 - -

Requirements:

• Ph.D. in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Software Engineering, DTI, Engineering, or a closely related field.
• Demonstrated expertise in Generative AI and Large Language Models (LLMs), including areas such as Transformers, diffusion models, GANs, VAEs, retrieval-augmented generation (RAG), prompt engineering, fine-tuning, and multimodal AI systems.
• Experience developing or applying modern AI/ML workflows using industry-standard tools and frameworks such as Python, PyTorch, TensorFlow, Hugging Face Transformers, LangChain, vector databases, and cloud-based AI platforms.
• Knowledge of AI deployment practices, including inference optimization, parameter-efficient training, model evaluation, safety, alignment, responsible AI, and scalable deployment architectures.
• Knowledge of emerging agentic AI systems and multi-agent orchestration frameworks for autonomous reasoning, planning, and task execution.
• Teaching experience at the graduate/undergraduate level in Artificial Intelligence, Machine Learning, Data Science, or related disciplines
• Demonstrated ability to translate complex AI concepts into applied, industry-relevant learning experiences through lectures, labs, projects, and case studies.
• Relevant industry experience in AI/ML development, applied Generative AI, MLOps, or AI product development is considered an asset.

Additional Information and/or Comments:

The course is online

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 vra.affairesprofessorales@uottawa.ca 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.