The Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division at King Abdullah University of Science and Technology (KAUST) invites applications for an instructional Faculty in the area of Computer Science and Engineering. Particular areas of interest are machine learning, artificial intelligence, computer architecture, embedded systems, and their applications. 

Applicants for the position must have a Ph.D. in Computer Engineering, Computer Science, Applied Mathematics,  Artificial Intelligence (AI) or a related field; emphasis on AI is preferred. Ideal candidates will have a track record of excellence in teaching and student mentorship at the graduate level. 

Instructional faculty are expected to teach multiple courses per year, possibly with multiple offerings of the same course per year. In addition to teaching responsibilities, Instructional Faculty may have professional and administrative responsibilities such as mentoring students, designing courses, proctoring placement and admissions tests or qualifying exams, sitting on divisional committees, or any combination of the foregoing. Proficiency in online teaching, online course preparation, and database integration is a plus.

Typical Teaching Responsibilities:

·        Develop, design, and deliver comprehensive courses aligned with university curricula and certification requirements.

·        Develop, design, and deliver comprehensive micro-credential courses aligned with industry standards and certification requirements.

·        Create engaging and interactive learning materials, including videos, presentations, quizzes, and hands-on activities.

·        Deliver high-quality instruction through in-person and online platforms catering to diverse learning needs.

·        Foster a supportive and collaborative learning environment, encouraging student engagement and participation.

·        Provide timely feedback and assessment to students to monitor their progress and offer constructive guidance.

·        Stay up-to-date with industry trends, best practices, and certification updates to ensure course content remains relevant and current.

·        Collaborate with colleagues and industry experts to enhance course content and teaching methodologies.

·        Participate in faculty development programs and continuous improvement initiatives.

KAUST is committed to maintaining a diverse faculty body and encourages applications from women faculty candidates. Because of the high volume of applications that we receive, only applications submitted through the online form will be considered. Applications will be evaluated as soon as they are received, and will receive full consideration until the positions are filled. 

About KAUST

KAUST is an international, graduate research university dedicated to advancing science and technology through interdisciplinary research, education, and innovation. Located in Saudi Arabia, on the eastern shores of the Red Sea, KAUST offers superb research facilities, generous baseline research funding, and internationally competitive salaries, together with unmatched living conditions for individuals and families. More information about KAUST academic programs, research activities, and community life are available at: http://www.kaust.edu.sa 

The CEMSE division at KAUST is recognized for its vibrant research programs and collaborative environment supported by KAUST’s international research collaboration network and KAUST’s advanced research facilities such as the Nanofabrication, the Imaging and Characterization, and the Supercomputing Core Facilities.

More information is available at: 

https://cemse.kaust.edu.sa

https://cemse.kaust.edu.sa/ece

https://cemse.kaust.edu.sa/cs

Required Qualifications:

• Ph.D. in Computer Engineering, Computer Science, Applied Mathematics, Artificial Intelligence (AI) or related field
• Strong expertise and experience in AI.
• Prior experience in curriculum development, instructional design, and teaching.
• Familiarity with online learning management systems and educational technology tools.
• Excellent communication, presentation, and interpersonal skills.
• Passion for student success and commitment to creating an inclusive learning environment.

Preferred Qualifications:

• Industry certifications related to AI.
• Experience teaching micro-credential courses or certification preparation programs.
• Demonstrated success in student outcomes and certification pass rates.

Required Documents to apply: 

• Cover letter
• A complete curriculum vitae  
• Reflective Teaching Statement 
• Teaching Evaluations, when applicable 
• Other Evidence Documents (e.g. peer review of your teaching, awards, course syllabi) 
• The names and contact information for at least 3 (preferably 5) references