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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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, the generated data can be used to optimize mineral extraction processes from existing mines within a geometallurgical framework. The position will mostly focus on the development of measurement protocols and data
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of bioinformaticians, computer scientists, biotechnologists, biologists, and biochemists. The successful candidate will also enjoy an environment aimed to facilitate progress in the research career: networking, student
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techniques such as XRD, SEM, TEM, BET, Raman spectroscopy, and electrochemical testing. Collaborate with interdisciplinary teams, including chemists, material scientists, and engineers. Manage day-to-day
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endeavor requires an unprecedented level of interdisciplinary eort. Our group, therefore, is an excellent choice for multidisciplinary scientists seeking to apply and innovate forefront technologies as part
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world for citations per faculty according to QS rankings and offers an unparalleled environment for innovation and research. For more information, visit www.kaust.edu.sa . About CREST The Center for Renewable Energy
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. · Collaborate with interdisciplinary teams, including chemists, material scientists, and engineers. · Manage day-to-day research activities, including experiment design, execution, and troubleshooting
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intelligence framework for RO systems. Candidates with background in conventional and innovative membrane-based technologies with data driven modeling approach are encouraged to apply for this position. We
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for the entire family and dependents, and free K-12 education at the KAUST international school. Dual appointments are welcome, including non-scientist spouses. We will be hiring a minimum of 3 Postdocs
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research in the field of machine learning, more specifically, deep learning and representation learning architectures. Application areas of ML include, but are not limited to, computer vision, natural