The Computer, Electrical, and Mathematical Sciences and Engineering Division at King Abdullah University of Science and Technology (KAUST) invites applications for a faculty position in Statistics at any level (Assistant, Associate, or Full Professor) beginning in the Fall of 2020. 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.
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 western 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. The generous social policy coupled to the top-quality research facilities have succeeded in attracting top international faculty, scientists, engineers, and students making KAUST into the only university worldwide where fundamental goal-oriented and curiosity-driven research is employed to address the world’s most pressing challenges related to water, food and energy sustainability as well as their impact on the environment.
More information about KAUST academic programs and research activities are available at http://www.kaust.edu.sa
The faculty position is in the Statistics Program (http://stat.kaust.edu.sa) within the Computer, Electrical, and Mathematical Sciences and Engineering Division. Currently, the Statistics Program has 7 core faculty and 10 affiliated faculty. We are primarily interested in applicants with strong background in the following area:
Statistical Data Science and AI: including network data analysis and high-dimensional statistics.
Strong candidates in any area of statistics are encouraged to apply too by clicking on the "Apply" button.
The successful candidates will have a doctoral degree in Statistics or other relevant fields with publications in top Statistics journals and subject-matter journals/conferences. Moreover, experience in interdisciplinary research and a strong publication record commensurate with the level of the post they apply for are expected. For senior positions, the evidence of track record in successful attracting external funding and independent research is essential. Women candidates are especially encouraged to apply.
Applicants should apply by clicking on the "Apply" button. Inquiries on the position should be sent to firstname.lastname@example.org Applications will be evaluated as soon as they are received, and taken into consideration until the positions are filled. Applications received by January 31, 2020, are guaranteed consideration.Continue reading
|Title||Faculty Positions in Statistical Data Science and AI for 2020|
|Employer||King Abdullah University of Science and Technology (KAUST)|
|Job location||King Abdullah University of Science and Technology, Thuwal|
|Published||December 16, 2019|
|Job types||Professor,   Lecturer / Senior Lecturer,   Assistant / Associate Professor,   Tenure Track  |
|Fields||Informatics,   Information Science,   Algorithms,   Artificial Intelligence,   Artificial Neural Network,   Computer and Society,   Computer Architecture,   Computer Communications (Networks),   Computer Graphics,    and 21 more. Cyber Security,   Computing in Mathematics, Natural Science, Engineering and Medicine,   Computing in Social science, Arts and Humanities,   Data Mining,   Data Structures,   Databases,   Distributed Computing,   Human-computer Interaction,   Information Systems (Business Informatics),   Operating Systems,   Parallel Computing,   Programming Languages,   Quantum Computing,   Software Engineering,   Theory of Computation,   Computational Sciences,   Game Design,   Big Data,   Machine Learning,   Machine Vision,   Computer Vision  |