APTPUO Winter 2027- MIA5100- (ONLINE)

University of Ottawa•Ottawa, ON

12d•Remote

About The Position

The University of Ottawa is seeking a Regular Part-Time Professor for the course FNDN & APP. MACHINE LEARNING (MIA5100) during the Winter 2027 semester. This online course is part of the Faculty of Engineering, specifically within the School of Engineering Design and Teaching Innovation. The course is designed for an expected enrollment of 30 students and carries 3 credits. The instruction language is English. The position is governed by the current APTPUO collective agreements.

Requirements

Ph.D. in AI, Machine Learning, DTI, Computer Science, Statistics, Mathematics, Engineering, or a closely related field with a strong focus on AI/ML.
Demonstrated industry experience in AI/ML, including demonstrated involvement in designing, building, and deploying machine learning systems in real-world applications.
Applied expertise in AI/ML, including areas such as Machine Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP), and model deployment, with evidence of real-world application in domains such as law, business, social sciences, or the arts.
Hands-on experience with industry-standard AI/ML tools and frameworks, including Python and libraries such as Scikit-learn, PyTorch or TensorFlow, NLP frameworks, Transformer-based models, and familiarity with ML pipelines and deployment workflows (e.g., Streamlit or similar tools).
Experience with cloud-based ML systems, including AWS, Azure, or Google Cloud, with demonstrated ability to deploy models in applied environments.
Experience delivering end-to-end AI/ML workflows, including data preparation, model development, evaluation, and deployment, in applied or industry settings.
Teaching experience in AI/ML at the graduate and/or undergraduate level.
Proven AI/ML experience in industry, as well as experience incorporating project-based or experiential learning approaches in AI/ML.

Responsibilities

Teach the course FNDN & APP. MACHINE LEARNING (MIA5100) online.
Incorporate project-based or experiential learning approaches in AI/ML.
Deliver end-to-end AI/ML workflows, including data preparation, model development, evaluation, and deployment, in applied or industry settings.
Potentially incorporate industry-standard AI/ML tools and frameworks, including Python and libraries such as Scikit-learn, PyTorch or TensorFlow, NLP frameworks, Transformer-based models, and familiarity with ML pipelines and deployment workflows (e.g., Streamlit or similar tools).
Potentially utilize cloud-based ML systems, including AWS, Azure, or Google Cloud, with demonstrated ability to deploy models in applied environments.