Adjunct Lecturer, Applied Deep Learning and AI (On-Campus, Fall '26) at Columbia University

New York, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

25 Jun, 26

Salary

13000.0

Posted On

27 Mar, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Deep Learning, Artificial Intelligence, Statistical Machine Learning, Supervised Learning, Support Vector Machines, Neural Networks, Convolutional Neural Networks, Word Embeddings, Attention Mechanisms, Transformers, Encoder-Decoder Architectures, Generative Adversial Networks, Reinforcement Learning, Python, TensorFlow, PyTorch

Industry

Higher Education

Description

Company Description Columbia University has been a leader in higher education in the nation and around the world for more than 250 years. At the core of our wide range of academic inquiry is the commitment to attract and engage the best minds to pursue greater human understanding, pioneering discoveries, and service to society. The School of Professional Studies at Columbia University offers innovative and rigorous programs that integrate knowledge across disciplinary boundaries, combine theory with practice, leverage the expertise of our students and faculty, and connect global constituencies. Through seventeen professional master's degrees, courses for advancement and graduate school preparation, certificate programs, summer courses, high school programs, and a program for learning English as a second language, the School of Professional Studies transforms knowledge and understanding in service of the greater good. Job Description 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. Qualifications Columbia University SPS operates under a scholar-practitioner faculty model, which enables students to learn from faculty possessing outstanding academic training and a record of accomplishment as practitioners in an applied industry setting. 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. Preferred Skills & Experience 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. Additional Information Salary range: $11,000 - $13,000 per semester long course Please submit a resume inclusive of university teaching experience. All your information will be kept confidential according to EEO guidelines. Columbia University is an Equal Opportunity Employer / Disability / Veteran Employee Job Category: Faculty Job Term: 2026 FALL Role: Lecturer, Part-time Department: Applied Analytics Division: Masters Program: Applied Analytics Academic Program: APAN

Responsibilities

Lecturers will lead class lectures, instructional activities, and classroom discussions, while also monitoring student concerns and evaluating/grading student work and assessments. They are expected to attend all class sessions and conduct office hours as part of their role as primary instructors.