20 estimation-methods "https:" "Computer Vision Center" Postdoctoral positions 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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mentor graduate students when needed. Where to apply Website https://apply.interfolio.com/183329 Requirements Research FieldEngineering » Materials engineeringEducation LevelPhD or equivalent Research
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the last 5 years or near completion); strong background in theoretical condensed matter physics; experience with analytical many-body methods, field theory, and/or numerical techniques is a plus. Details
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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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on the development of new methods integrating a variety of data types (remote sensing, geology, geophysics, geochemistry) for geological modelling and advanced exploration targeting of mineral deposits
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visit: https://www.kaust.edu.sa https://www.kaust.edu.sa/en/live/community-life https://bese.kaust.edu. At KAUST, we attract people from all around the world who want to create impact beyond their own
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integration methods for the different data types. In terms of applications, the candidate will be free to choose their own case study(s). Additionally, close collaboration with other group members is expected
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at the Mechanics of Composites for Energy and Mobility Lab. (MCEM, https://composites.kaust.edu.sa ). Field of study A Postdoctoral opening is available in the area of Insitu Multiphysics
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technologies. Key Responsibilities: Develop and optimize hard carbon synthesis processes using bio-based and non-bio-based precursors. Explore innovative methods to enhance material properties for energy storage
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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