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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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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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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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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 Innovations in Desalination Technologies Theme at Water Desalination and Reuse Center (WDRC) in King Abdullah University of Science and Technology (KAUST), invites suitably qualified applicants holding a PhD in chemical, environmental or process engineering, to apply for a full-time...
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The VCC center at KAUST is looking for research scientists in Prof. Wonka's research group. The topics of research are computer vision, computer graphics, and deep learning. A suitable candidate
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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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experience with molecular and cell biological methods. The applicant will work to advance our knowledge of viral vector production in versatility, stability and efficacy with modern techniques from plasmids
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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
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rigorous 3D modeling tool to model multiphase fluid flow within the wellbore using a computational fluid dynamics (CFD) solvers, such as OpenFOAM and Ansys-fluent. The model should account for thermodynamics