27 parallel-computing-numerical-methods-"Prof" research jobs at King Abdullah University of Science and Technology in Saudi Arabia
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be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and Stochastic Optimal Control”. Background or expertise in one or more of the following
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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 successful candidate will join the Integrated Intelligent Systems (I2S) Lab , led by Prof. Gianluca Setti, and contribute to cutting-edge research at the intersection of AI and IoT. We are seeking talented
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conventional simulators. Finite element-based methods such as Mixed-Finite-Element or Control-Volume methods are convenient thanks to their suitability for complex unstructured grids. Applications are sought
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Prof. Husam N. Alshareef , Chair of the Center of Excellence for Renewable Energy and Storage Technologies (CREST) at KAUST, is seeking highly qualified postdoctoral researchers for Hard Carbon
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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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Three postdoctoral positions at the King Abdullah University of Science and Technology (KAUST) under the mentorship of Prof. Jürgen Schmidhuber.This project, located at the intersection
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Prof. Husam N. Alshareef , Chair of the Center of Excellence for Renewable Energy and Storage Technologies (CREST) at KAUST, is seeking postdoctoral researchers with experience in the field
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approaches. We are now recruiting 3 postdoctoral researchers (https://postdoc.kaust.edu.sa/Pages/Home.aspx ) with a strong computational and/or theory background to develop novel computational methods. We
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Laboratory of Applied Virology and Precision Medicine, led by Prof. Fabian Schmidt Who we are: We are impact-driven bioengineers who tackle the challenge of reproducible precision delivery