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, statistical learning, and deep learning - Applications in environmental and life sciences Here are a few recent publications from the group, which form the basis of some of the upcoming research projects
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We welcome candidates with a PhD in Electrical or/and Computer Engineering, or a closely related field, with a strong background in the theory and practice of one or more of Power Systems
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holding a PhD in chemical, environmental or process engineering, to apply for a full-time post-doctoral fellowship position in the field of water desalination, focusing on the development of an artificial
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related to the four research priorities of KAUST Up to 4 years of post-PhD experience Strong communication skills and fluency in English Submission of an innovative research proposal Support letter from a
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to 4 years of post-PhD experience Strong communication skills and fluency in English Submission of an innovative research proposal Support letter from a KAUST host PI Applicants should apply through
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for chemical and morphological characterization. The candidate should preferentially have a PhD in chemistry, polymer science, or membrane preparation and characterization. Besides the salary (contract of 2
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Environment exposed to high pressure / high temperature / mechanical and environmental loading. Develop accelerated multiscale approaches using AI-based scale transition and homogenization. Develop AI-based
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configurations in Energy applications. Lead the development of a unique experimental platform for in-situ characterization under environmental loading of laminated structures. Plan and execute research tasks
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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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effective solution to mitigate GHG emissions that can be deployed at large scales. CCS may enable the industry to continue operating with reduced environmental footprint. The objective of this project is to