35 parallel-processing positions at King Abdullah University of Science and Technology
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The Statistics (STAT) program in the Computer, Electrical, and Mathematical Sciences and Engineering Division (https://cemse.kaust.edu.sa) at King Abdullah University of Science and Technology
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, photovoltaics, porous materials for carbon capture and membranes, clean combustion, atmospheric modeling, new polymers and composite for smart materials and structures, catalysis and catalytic processes. KAUST is
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of mineral resources in Saudi Arabia by developing innovative approaches to mineral exploration, mining, and mineral processing. The working group will initially consist of 1 PhD student and 2 MSc students
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technologies. Key Responsibilities: Develop and optimize hard carbon synthesis processes using bio-based and non-bio-based precursors. Explore innovative methods to enhance material properties for energy storage
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and synthesis of microporous polymers and their applications in electrochemical processes for energy storage and conversion. You will independently lead a research direction within the group, while
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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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spectroscopy, and PPMS. Use cleanroom nanofabrication processes to build 2D-material-based electronic devices. Design, execute, and troubleshoot experiments. Publish research findings in high-impact journals and
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or non-bio-based sources, with applications in energy storage and other emerging technologies. Key Responsibilities: · Develop and optimize hard carbon synthesis processes using bio-based and non-bio
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using a combination of multimodal imaging, computer vision, and lab automation platforms that govern entire workflows (e.g. ThermoFisher momentum software scheduling Hamilton liquid handlers and high-end
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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict