42 computational-geometry positions at King Abdullah University of Science and Technology in Saudi Arabia
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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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Three postdoctoral positions at the King Abdullah University of Science and Technology (KAUST) under the mentorship of Prof. Jürgen Schmidhuber.This project, located at the intersection of Reinforcement Learning and Material Science, focuses on developing innovative solutions for the discovery...
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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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As part of a major initiative to strengthen its research in marine science, the Marine Science Program (MarS) in the Biological and Environmental Science and Engineering (BESE) Division at KAUST is
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The Plant Science Program in the Biological and Environmental Science and Engineering (BESE) Division at King Abdullah University of Science and Technology (KAUST) invites applications for faculty
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The Bioengineering (BioE) Program in the Biological and Environmental Science and Engineering (BESE) Division at King Abdullah University of Science and Technology (KAUST) bridges the University’s
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The Bioengineering (BioE) Program in the Biological and Environmental Science and Engineering (BESE) Division at King Abdullah University of Science and Technology (KAUST) bridges the University’s
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computational resources. It is the leading university in citation per faculty according to the QS Rankings. Further information can be found at www.kaust.edu.sa .
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As part of a major initiative to strengthen its research in Marine Science, the Marine Science Program (MarS) in the Biological and Environmental Science and Engineering (BESE) Division at KAUST is
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containment. The prohibitively high computational cost of such simulations necessitates the development of efficient and robust surrogate models for general GCS modeling tasks, especially when inverse modeling