20 digital-biosignal-processing Postdoctoral research jobs at King Abdullah University of Science and Technology in Saudi Arabia
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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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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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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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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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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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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
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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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. Responsibilities Development and optimization of perovskite-based solar cells at different levels. Developing large-area perovskite solar cells utilizing KPV-LAB's baseline processes. Performing accurate device
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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