The CEMSE division at KAUST is looking for people from Saudi Arabia and around the world who want to create impact beyond their own achievements. We look for "people of the world" who will uphold our values of Achievement, Passion, Inspiration, Citizenship, Diversity, Integrity and Openness.
Join KAUST to be part of discoveries that address global challenges in five key areas: food, water, energy, environment, and digital. To contribute to science and innovation and become part of a uniquely international community where you and your family will thrive.
Faculty Positions 2024
The Computer, Electrical, and Mathematical Sciences and Engineering Division at King Abdullah University of Science and Technology (KAUST) invites applications for faculty positions in Applied Analysis, Partial Differential Equations, Applied probability, and Numerical Analysis, Mathematical Foundations of Data Science, Scientific Computing, Optimization, Applied Geometry, and Related Fields at any level (Assistant, Associate, or Full Professor), beginning in the Fall of 2022. Candidates applying for a position of Assistant Professor should have an excellent potential for high-impact research. Candidates applying for Associate and Full Professor positions should have a distinguished track record in research and a strong commitment to service, mentoring, teaching at the graduate level, and making an impact in interdisciplinary research. Candidates working on the interface of KAUST initiatives, including artificial intelligence, climate and livability, resilient computing and cybersecurity, and smart health, are encouraged.
Candidates must have a doctoral degree in Mathematics or other relevant fields with publications in top journals and subject-matter journals/conferences. Moreover, experience in interdisciplinary research and a strong publication record commensurate with the level of the post he/she applies for are expected. The evidence of a track record in successfully attracting external funding and independent research for senior positions is essential. Women candidates are especially encouraged to apply.
Apply hereThe 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.
Apply hereThe Electrical and Computer Engineering (ECE) Program in the Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division at the King Abdullah University of Science and Technology (KAUST) invites applications for faculty positions at the rank of Assistant Professor (exceptional candidates at the Associate Professor level may be considered).
Candidates in the following areas of Electrical and Computer Engineering are strongly encouraged to apply:
- Analog integrated circuits, low-power and high-precision interface circuits, telecommunication and RF circuits, hardware for Machine Learning and AI, data converters and EDA, and edge computing. Applications in harsh environments and/or power circuits are particularly desirable
- Power electronics, power systems, energy storage systems, electric automotive systems, smart grid technologies, high-efficiency alternative and renewable energy technologies to assure secure access to energy resources while addressing climate change concerns.
- Embedded systems, fault-tolerant computing, hardware-friendly machine learning, low-power computing to enhance safety, efficiency and accessibility, in-vehicle computing, medical and wearable devices, modern cities, and smart industries.
- Robotic and autonomous systems and their applications in areas like (among others) infrastructure monitoring, underwater exploration, smart manufacturing, precision agriculture, and health care.
Candidates with portfolios that align with Saudi Arabia’s national priorities for research, development, and innovation (RDI), namely, Health and Wellness, Sustainability and Essential Needs, Energy and Industrials, and Economies of the Future will be given the utmost priority (see also https://www.rdia.gov.sa/index.en.html).
Apply hereThe current challenges that confront the statistical data sciences deal with the need to efficiently process massive data build powerful statistical models, and develop efficient computational tools. There are opportunities to develop impactful research that can address substantive inferential and forecasting problems on a wide array of complex processes including biological, social, physical, epidemiological, climatological and environmental.
The Statistics Program (https://cemse.kaust.edu.sa/stat) at KAUST aims to contribute to statistical data science by developing modern approaches for conducting rigorous inference, which will help to advance research in these disciplines. Through our methods, researchers and policymakers will be equipped with information along with measures of uncertainty that are necessary for making sound decisions in a timely manner.
To address these current global needs, the Statistics Program at KAUST will be hiring multiple open-rank positions with expertise on these two modern core areas:
- Statistical Data Science with emphasis on high dimensional statistics.
- Statistical Data Science with computational methods.