Excelsior University, an institutionally accredited, online private not-for-profit institution of higher learning, seeks dynamic applicants to join our remote part-time faculty to teach Computer Science and related courses.

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 Excelsior University 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.

Funciones y responsabilidades: 

• Teach assigned course as prescribed, making no changes to content or implementation guidelines.
• Demostrar experiencia en la materia mediante una participación constructiva y sustantiva en los debates/actividades de aprendizaje de los estudiantes y compartiendo sus conocimientos y experiencia.
• Desafíe el propio pensamiento de los estudiantes y establezca conexiones entre la teoría y la práctica a través de áreas de conocimiento dispares en entornos diversos y entre puntos de vista divergentes.
• 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.

Cualificaciones: Para desempeñar este trabajo con éxito, un individuo debe ser capaz de realizar cada deber esencial de forma satisfactoria. Los requisitos que se enumeran a continuación son representativos de los conocimientos, la destreza y/o la capacidad necesarios. Se pueden realizar adaptaciones razonables para que las personas con discapacidades puedan realizar las funciones esenciales.

• 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.
• Gran capacidad de comunicación interpersonal y de resolución de problemas.
• Commitment to Excelsior University's mission and CARES Credo.

Tasa de pago: 

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.