Assistant / Associate / Full Professor in Statistics
- Thuwal, Saudi Arabia
- 11 May 2023
- End of advertisement period
- 10 Jun 2023
- Academic Discipline
- Physical Sciences, Mathematics & Statistics
- Job Type
- Academic Posts, Professors / Chairs, Principal / Senior Lecturers / Associate Professors, Lecturers / Assistant Professors
- Contract Type
- Full Time
King Abdullah University of Science and Technology: Faculty Positions: Computer, Electrical and Mathematical Science and Engineering Division (faculty): Statistics (faculty)
Location Saudi Arabia
The 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 (http://stat.kaust.edu.sa) 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 is 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 in the following two modern core areas, although strong candidates with expertise in any area of Statistical Science are also encouraged to apply.
Statistical Data Science with emphasis on high dimensional statistics
Modern technological advances enable the collection of massive data from various fields including networks (transportation, communication, social), biological, epidemiological and climate. Some of these data are high-dimensional objects such as networks and curves. One goal is to extract low-dimensional representations of these complex objects that help to understand the underlying complex processes. Another is to develop statistical tools and models in order to quickly make accurate predictions. This requires building high-dimensional statistical models that capture the intrinsic features for visualization, inference, and predictions. This core area has a high potential impact on the areas mentioned above. Through these complex models that efficiently extract information from massive data, engineers can anticipate disruptions in transportation networks, climate researchers are able to forecast (and hence prepare for) drought and wildfires and public health experts are able to track and predict the spread of infectious diseases. Applicants in this core area are expected to be developing a novel framework and inferential methods that deal with the challenges of high dimensional statistics. Applicants should aim to make an impact in modeling and forecasting in areas including, but not limited to, public health, climate, social sciences, and cybersecurity.
Statistical Data Science with emphasis on computational methods
Most of the research in statistics within the last decades has been focused on improving the computational methods and, often in tandem, developing statistical methods that make inference scale well computationally. From the computational side, this includes methods based on stochastic gradient; methods for simulation-based inference such as Markov chain Monte Carlo methods, ABC, particle filtering, and ensemble Kalman filtering; to methods that are by design approximate such as Variational Bayes and Laplace approximations; or those interpreting numerical methods as learning algorithms like in probabilistic numerics. We are in the early time of parallel computing, so it is beneficial that new methods developed also scale and run well in a high-performance computing environment. Applicants in this core area are expected to develop cutting-edge research with the aim of developing methods and models that have an impact of pushing the boundaries for the use of statistics in areas including, but not limited to, smart health, artificial intelligence, energy, climate, and robotics.
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 has succeeded in attracting top international faculty, scientists, engineers, and students making KAUST 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.
Applicants should have a Ph.D. degree in Statistics (or other relevant fields). The Statistics Program at KAUST welcomes especially applications from early-career academics (Assistant to early Associate Professor). However, Full Professors with internationally recognized research within at least one of these two areas are also encouraged to apply. Applicants should have a strong commitment to mentoring, teaching at the graduate level, service, and making an impact in interdisciplinary research, in particular on KAUST initiatives including artificial intelligence, climate and livability, resilient computing and cybersecurity, and smart health.
Applications from women with expertise in these core areas are especially welcome.
Applications should be submitted in interfolio.
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