36 digital-biosignal-processing positions at King Abdullah University of Science and Technology
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, digital twins, and machine learning into process systems of industrial and societal relevance. Furthermore, we expect research proposals to align closely with the UN Sustainable Development Goals and to
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Education: Ph.D. or M.S. in Computer Science, AI, Computer Vision, or related field Experience: 3+ years in computer vision and deep learning, with specific focus on microscopic imaging, generation
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The Computer Vision-Core Artificial Intelligence Research (Vision-CAIR ) group led by Prof. Mohamed Elhoseiny at the CS Program of the King Abdullah University of Science and Technology (KAUST) is
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The VCC center at KAUST is looking for postdoctoral researchers and research scientists in Prof. Wonka's research group. The topics of research are computer vision, computer graphics, and deep
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/molecular screens/assays (e.g., live-cell imaging, bioluminescence assays, digital PCRs, NGS) and the translation of gained insights into the generation of novel cell lines and vector formats (e.g., synthetic
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to finding solutions for some of the most pressing scientific and technological challenges in the world as well as Saudi Arabia in the areas of food and health, water, energy, environment and the digital
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Serve as the Lead for the team ensuring smooth operation of the Linux cluster consisting of 300+ GPU/CPU compute nodes including parallel filesystems and high-performance network. This is partly
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is to develop a modeling framework including the use of Random-Walk method to predict NMR measurements, pore-scale finite-element modeling on 3D digital models, generated from CT-images to predict
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for the following research themes: Heterogeneous Catalysis Metal Capture from Water Electrochemical Water Splitting Process Design and Scale-up
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, the generated data can be used to optimize mineral extraction processes from existing mines within a geometallurgical framework. The position will mostly focus on the development of measurement protocols and data