Adjunct Lecturer, Applied Deep Learning and AI (Online, Fall '26)
About the position
The School of Professional Studies seeks experienced industry professionals to serve as part-time Lecturer for a graduate-level course in Applied Deep Learning and AI. This advanced course delves into deep learning, blending key elements from Statistical Machine Learning. Students will gain a solid foundation in supervised learning and other related algorithms and methods. Topics covered include Support Vector Machines, Neural Networks, Convolutional Neural Networks (CNN), word embeddings, attention mechanisms, transformers, encoder-decoder architectures, Generative Adversial Networks (GAN), and Reinforcement Learning. Practical applications will demonstrate how to prepare, train, test, and validate models. Lecturers are the primary instructors for courses and an invaluable component of the faculty community.
Responsibilities
• Lead class lectures, instructional activities, and classroom discussions.
• Attend all class sessions.
• Monitor and address student concerns and inquiries.
• Evaluate and grade student work and assessments.
• Conduct office hours.
Requirements
• Doctorate degree in Computer Science, Data Science, or a related field.
• Proficient in Python and familiar with deep learning frameworks (e.g., TensorFlow, PyTorch).
• Deep Learning Knowledge: Strong understanding of CNNs, RNNs, LLMs, Reinforcement Learning, and model evaluation techniques.
• Hands-on experience with deep learning projects and data manipulation.
Nice-to-haves
• 10+ years of related professional experience.
• 2+ years of University teaching experience, ideally at the graduate level.
• Strong verbal and written skills for explaining concepts clearly.
Apply Now
Apply Now
The School of Professional Studies seeks experienced industry professionals to serve as part-time Lecturer for a graduate-level course in Applied Deep Learning and AI. This advanced course delves into deep learning, blending key elements from Statistical Machine Learning. Students will gain a solid foundation in supervised learning and other related algorithms and methods. Topics covered include Support Vector Machines, Neural Networks, Convolutional Neural Networks (CNN), word embeddings, attention mechanisms, transformers, encoder-decoder architectures, Generative Adversial Networks (GAN), and Reinforcement Learning. Practical applications will demonstrate how to prepare, train, test, and validate models. Lecturers are the primary instructors for courses and an invaluable component of the faculty community.
Responsibilities
• Lead class lectures, instructional activities, and classroom discussions.
• Attend all class sessions.
• Monitor and address student concerns and inquiries.
• Evaluate and grade student work and assessments.
• Conduct office hours.
Requirements
• Doctorate degree in Computer Science, Data Science, or a related field.
• Proficient in Python and familiar with deep learning frameworks (e.g., TensorFlow, PyTorch).
• Deep Learning Knowledge: Strong understanding of CNNs, RNNs, LLMs, Reinforcement Learning, and model evaluation techniques.
• Hands-on experience with deep learning projects and data manipulation.
Nice-to-haves
• 10+ years of related professional experience.
• 2+ years of University teaching experience, ideally at the graduate level.
• Strong verbal and written skills for explaining concepts clearly.
Apply Now
Apply Now