Part-Time Online Faculty - Computer Science at Excelsior University | ZeeCV Jobs
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Part-Time Online Faculty - Computer Science

United States Aug 25, 2026
Online-Faculty Computer-Science-Instructor Adjunct-Professor Adult-Education Adjunct-Faculty-Computer-Science Part-Time-Online-Instructor Online-Adjunct-Faculty Online-Adjunct-Professor Computer-Science-Faculty Academic-Programs

Job Description

Candidates should have advanced knowledge and expertise in Computer Science, with a demonstrated passion for teaching adult learners and helping them thrive academically. Preference will be given to candidates with experience in various areas of Computer Science and a proven track record of excellence in teaching adult learners in an online environment. This position reports directly to the Department Chair of IT and Cybersecurity.

Teaching at means embracing a commitment to teaching excellence and delivering high-quality instruction to non-traditional students. Our faculty play a vital role in empowering students to acquire the knowledge and skills necessary to excel as leaders in their professions. Faculty members foster diverse perspectives, encourage critical thinking and problem-solving, and help students challenge assumptions while connecting research to real-world practice. They are deeply engaged in teaching, mentoring, and guiding students toward academic and professional success.

Duties and Responsibilities:

  • Teach assigned course as prescribed, making no changes to content or implementation guidelines.
  • Demonstrate subject matter expertise through constructive and substantive involvement with student discussions/learning activities and sharing of expertise and experience.
  • Challenge students’ own thinking and form connections between theory and practice across disparate areas of knowledge in diverse settings, and among divergent viewpoints.
  • Create an effective, engaged, and vibrant online learning community by maintaining a frequent and meaningful online/email presence in courses.
  • Adhere to established minimum teaching and engagement expectations, and to all University and School of Graduate Studies policies as outlined in the Faculty Handbook.
  • Offer timely formative and summative feedback on student assignments. Set clear academic standards, provide constructive feedback throughout the course, and provoke critical thinking.
  • Commit an average of 134-140 hours of work per 3-credit course assigned.
  • Provide prompt, substantive, and timely responses to student needs and communications.
  • Be familiar with the Canvas learning management system and all support services available to students.
  • Demonstrate skill in understanding cultural differences and a commitment to diversity, equity, and inclusion. Experience working with individuals from diverse racial, ethnic, and socioeconomic backgrounds.

Qualifications: To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

  • An earned master's degree in computer science or related field required. Doctorate preferred.

  • Demonstrated significant industry and/or experience in the field of Data Science, Artificial Intelligence, Networking and Cloud Computing, or Software Development.

  • Teaching or training experience in Data Science, Artificial Inelegance, Networking and Cloud Computing, Software Development, or related fields.

  • Strong interpersonal communication and problem-solving skills.

  • Commitment to 's mission and CARES Credo.

Rate of Pay:

1. A flat rate of $1000 per credit for a section of at least 10 undergraduate students or 8 graduate students. Courses that fall under these student headcounts are

considered low enrolled courses.

2. Low enrolled courses will be paid on a directed study rate ($250/student for undergraduate courses and $300/student for graduate courses) based on the number of students enrolled in the course section at the close of late registration.