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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
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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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. These workflows will then be applied in relevant Saudi Arabian contexts to help discover new ore deposits. The position will combine techniques from geological modelling, geostatistics, machine learning, and
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, an annual travel allowance, 30 paid vacation days, and other generous benefits. KAUST is a vibrant and international community, with many opportunities for social, sporting, and learning activities outside
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quantum physics , Physics-informed machine learning , Quantum chaos and thermalization , Quantum Computation , Quantum Computing , Quantum Condensed Matter Theory , Quantum criticality and phase transitions
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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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» Physical chemistry Engineering » Materials engineering Engineering » Chemical engineering Researcher Profile First Stage Researcher (R1) Recognised Researcher (R2) Positions PhD Positions Application
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directions and goals, within remit of original project proposal. Supporting of less experienced members of the project team eg PhD students Contributing to the documentation of all research results