Regular Part-Time Professor
University of Ottawa • Ottawa, ON25d • Remote
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APTPUO Fall 2026- MIA5150-REPOST (ONLINE) Topic (Generative AI and (LLMs)
University of Ottawa•Ottawa, ON
•Remote
About The Position
This posting is for a Regular Part-Time Professor position to teach the course MIA5150: Generative AI and Large Language Models during the 2026 Fall Semester. The course will be delivered online and is expected to have an enrolment of 30 students. The course covers the foundations and practices of Generative AI and LLMs, including model development, deployment, various generative model families (Transformers, autoregressive models, diffusion models, GANs, VAEs), and their multimodal applications. It also delves into modern techniques like fine-tuning, parameter-efficient training, in-context learning, retrieval-augmented generation (RAG), and multi-agent systems for complex reasoning. Key practices in prompt engineering, inference optimization, safety, alignment, and responsible AI deployment will be discussed, along with practical applications across diverse domains. Prerequisites include MIA5100, MIA5126, or equivalent.
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.
Nice To Haves
- Relevant industry experience in AI/ML development, applied Generative AI, MLOps, or AI product development is considered an asset.
Responsibilities
- Teach the course MIA5150: Generative AI and Large Language Models.
- Deliver lectures, labs, projects, and case studies to translate complex AI concepts into applied, industry-relevant learning experiences.
- Develop and deliver course content covering the full lifecycle of Generative AI and LLMs from model development to deployment.
- Discuss modern techniques such as fine-tuning, parameter-efficient training, in-context learning, contrastive learning, retrieval-augmented generation (RAG), and multi-agent systems.
- Cover key practices in prompt engineering, inference optimization, safety and alignment, and responsible AI deployment.
- Facilitate learning for an expected enrolment of 30 graduate/undergraduate students.
- Potentially translate complex AI concepts into applied, industry-relevant learning experiences through lectures, labs, projects, and case studies.
Benefits
- Vacation pay
- Statutory pay
- Retroactive pay after ratification of a new collective agreement
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What This Job Offers
Job Type
Part-time
Career Level
Mid Level
Education Level
Ph.D. or professional degree
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