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be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and Stochastic Optimal Control”. Background or expertise in one or more of the following
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conventional simulators. Finite element-based methods such as Mixed-Finite-Element or Control-Volume methods are convenient thanks to their suitability for complex unstructured grids. Applications are sought
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on the development of new methods integrating a variety of data types (remote sensing, geology, geophysics, geochemistry) for geological modelling and advanced exploration targeting of mineral deposits
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integration methods for the different data types. In terms of applications, the candidate will be free to choose their own case study(s). Additionally, close collaboration with other group members is expected
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years of post-PhD experience (Ph.D. awarded after January 2022) Demonstrated strong communication skills and fluency in English Submission of an innovative research proposal aligned with KAUST priorities
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· PhD in Materials science, chemistry, physics, polymer science, or related field · Strong background in ferroelectrets, piezoelectric materials, voided charged polymers or piezocomposites
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environment dediacated to the wellbeing and career success of Fellows and their families. Eligibility Requirements Ph.D. in a discipline related to KAUST’s research priorities Up to 4 years of post-PhD
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Applicants must have a PhD in Computer Engineering, Computer Science, or Electrical and Computer Engineering, and have published their research in prestigious conferences and journals in related
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approaches. We are now recruiting 3 postdoctoral researchers (https://postdoc.kaust.edu.sa/Pages/Home.aspx ) with a strong computational and/or theory background to develop novel computational methods. We
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experience in agronomy, soil science, engineering methods, and precision agriculture in semi- and hyper-arid regions. Specifically, we will be testing the effects of soil amendment technologies pioneered by