39 digital-signal-processing positions at King Abdullah University of Science and Technology
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, and 2D/3D detection along with point cloud processing Required Technical Expertise: ○ Deep Learning Frameworks: PyTorch, TensorFlow ○ Computer Vision Libraries: OpenCV, scikit-image
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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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encryption, authentication, and secure data transmission Integrate AI/ML models into web interfaces with real-time inference capabilities Create data visualizations for 2D, 3D, and point cloud data
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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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university-level studies. We do not ask for more information/documents at this point (but you can provide more if you want). Should your profile be shortlisted, we will ask for further information if there is
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airborne particle control (HVAC, AHU, CDA, PVAC) Chilled, waste and DI water systems Process gases and gas abatement Responsible for Gas Cylinder (toxic and corrosive) ordering, changes and ensuring safe but
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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