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-based precursors. · Explore innovative methods to enhance material properties for energy storage applications and other emerging technologies. · Conduct detailed structural, chemical, and
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, numerical simulators with coupled physics (flow/reactive/geo-mechanics/thermal) have been developed and often applied to predict the long-term fate of the injected carbon dioxide to ensure its secure
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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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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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/Online. A project at the Composites Lab is characterized by the amalgamation of experimental and computational/modeling mechanics and encompasses people with very different backgrounds to ensure we capture
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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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generous funding for research, a cutting-edge genomics facility, and top-tier high-performance computing infrastructure, fostering an excellent environment for genomics-based research. The position is fully
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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
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The Innovations in Desalination Technologies Theme at Water Desalination and Reuse Center (WDRC) in King Abdullah University of Science and Technology (KAUST), invites suitably qualified applicants holding a PhD in chemical, environmental or process engineering, to apply for a full-time...
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We invite applications for multiple Postdoc positions in the Accelerated Connected Computing Lab (ACCL) at KAUST! The new research group, supervised by Suhaib Fahmy explores hardware acceleration