Adjunct Associate Faculty, Artificial Intelligence of Things (AIOT) (On-Campus, Fall '26)

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 twenty 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.

Seeking analytics professionals to serve as a part-time Associate for a graduate-level course on Artificial Intelligence of Things (AIOT). An Associate is a faculty line junior to a Lecturer, that provides subject matter expertise and supports the instructional process for a course section. Serving as an Associate is an outstanding way to gain exposure to graduate-level teaching at Columbia University.

This course provides a comprehensive understanding of IoT technologies and their integration with AI and robotic systems. Students will explore IoT architecture, key components, and communication protocols, while gaining hands-on experience with IoT platforms, sensors, and data acquisition devices. The curriculum emphasizes practical AIoT applications for real-time decision-making in manufacturing, public safety, smart cities, healthcare, etc., and addresses the ethical considerations of these technologies.

Combining conceptual learning with practical assignments, the course features weekly lectures and readings on IoT fundamentals and applications, with biweekly quizzes to assess conceptual understanding. Students will further apply their learning through individual assignments and a group term project, ensuring a robust foundation in IoT analytics and AI-powered robotic automation.

Columbia University SPS operates under a scholar-practitioner faculty model, which enables students to learn from faculty possessing outstanding academic training as well as a record of accomplishment as practitioners in an applied industry setting.

Responsibilities

Attend all class sessions, assist with instruction, lead breakout sessions, facilitate discussions.; Evaluate, grade student work and assessments as requested by the course Lecturer.; Monitor and address student concerns and inquiries.

Requirements

Graduate degree in a relevant field (Computer Science, Electrical/Computer Engineering, Data Science, Applied Analytics, Information Systems, or closely related).; 2+ years of professional experience in one or more of: IoT/embedded systems, edge computing, cloud IoT platforms, data analytics/ML, or adjacent industries (e.g., smart devices, manufacturing/IIoT, healthcare IoT).; Proficiency with Python and Jupyter/pandas/matplotlib for analytics and data visualization; Strong knowledge of AI fundamentals and experience with machine learning applications; Strong communication and student mentorship skills, responsiveness on Slack/office hours, ability to manage hardware kits and demos.

Nice to have

University teaching or TA experience, especially in graduate or professional programs.; University teaching/TA experience in project-based or lab-based courses.; Proficiency with MicroPython for device programming; Practical skills with Thonny, UIFlow2, or VS Code, serial/USB drivers (e.g., CP210x), and a macOS/Windows terminal; Hands-on familiarity with ESP32-class microcontrollers (M5Stack Core2 preferred) and MQTT publish/subscribe patterns.; Working knowledge of AWS IoT Core (Things, certificates/policies, device shadows) and comfort with basic AWS services used for analytics (e.g., S3, QuickSight/Athena/Glue or equivalent).