Nontenure Faculty Pool - Manufacturing Engineering

Texas State University • San Marcos, TX

70d • Onsite

About The Position

The Ingram School of Engineering seeks to develop a pool of candidates for possible temporary/nontenured faculty positions to teach in the Manufacturing Engineering Program. For information about the Manufacturing Engineering program please visit https://www.engineering.txst.edu/programs/Manufacturing.html. Temporary/nontenured faculty are appointed to teach one to four courses per semester. Teaching appointments are based upon student demand on a semester-by-semester basis. Eligibility for annual reappointment is contingent upon satisfactory annual performance evaluations during the term period, in addition to continuity of funding and school need. Successful candidates will teach at the San Marcos campus. This posting is for the fall 2025, spring 2026, and summer 2026 semesters. Please note that this posting is not intended to convey that such a need currently exists. Applicants applying for this pool faculty position will be available to the Manufacturing Engineering Program search committee for consideration in case the need arises.

Requirements

• A minimum of a master's degree in Manufacturing Engineering, Mechanical Engineering or a closely related engineering field.
• Strong academic background in one of the following areas: Engineering Materials, Computer Aided Engineering (CAE), Control Systems, Robotics, Cyber Security in Manufacturing Systems, Manufacturing Processes, Mechanics of Materials, Engineering Graphics (CAD), and Computer Aided Design and Manufacturing (CAD/CAM), Dynamics of Machinery, Additive Manufacturing, Manufacturing Systems Design, Advanced Robotics.
• Excellent written and oral communication skills

Nice To Haves

• A Ph.D. in Manufacturing Engineering, Mechanical Engineering, or a closely related engineering field.
• Previous experience teaching undergraduate engineering courses as the instructor of record.
• Expertise with disruptive technologies, AI, and Machine Learning.
• Industrial experience.

What This Job Offers

• Job Type: Part-time
• Industry: Educational Services
• Number of Employees: 51-100 employees