26 engineering-image-processing-phd Postdoctoral positions at King Abdullah University of Science and Technology
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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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of lithium-ion battery recycling. The focus of this position is on material recovery from cathode materials through a groundbreaking filtration and extraction processes. Key Responsibilities: Participate in
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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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groundbreaking filtration and extraction processes. Key Responsibilities: Participate in and lead the development of lithium-ion battery recycling technologies. Develop advanced separation technologies including
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. Develop sensing principles, data acquisition, and measurement systems. Upscale technology and validate its integration into a variety of structures, including structures for mobility, energy, and civil
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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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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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The Machine Learning (ML) group led by Professor Ricardo Henao at KAUST (King Abdullah University of Science and Technology) has multiple openings for Postdoctoral level positions to conduct
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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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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