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.
• 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.

Requisitos: Para desempeñar este puesto con éxito, el candidato debe ser capaz de realizar cada una de las funciones esenciales de manera satisfactoria. Los requisitos que se enumeran a continuación reflejan los conocimientos, las habilidades y/o las capacidades necesarias. Se podrán realizar adaptaciones razonables para que las personas con discapacidad puedan desempeñar las funciones esenciales.

• Se requiere un máster en informática o un campo relacionado. Se prefiere el doctorado.
• 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.
• Compromiso con la misión de la Universidad Excelsior y el Credo CARES.

Tasa de pago: 

1. Una tarifa fija de 1.000 dólares por crédito para una sección de al menos 10 estudiantes de grado u 8 de postgrado. Los cursos que quedan por debajo de estos recuentos de estudiantes se considerados cursos de baja matrícula. 2. Los cursos de baja matrícula se pagarán con una tasa de estudio dirigido (250 $/estudiante para los cursos de licenciatura y 300 $/estudiante para los cursos de postgrado) basada en el número de estudiantes matriculados en la sección del curso al cierre de la inscripción tardía.