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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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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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. 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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research partners. Support the supervision of PhD and MSc students.
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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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are especially interested in candidates with a PhD in Materials Science, Chemistry, Physics, Chemical Engineering, or a related field, and with a solid experimental background in organic semiconductors, polymer
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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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-performance SWIR and exploratory room-temperature MWIR detection. Candidates should hold a PhD in chemistry, materials science, electrical engineering, applied physics, or a related field, with expertise in
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manipulation for high-performance SWIR and exploratory room-temperature MWIR detection. Candidates should hold a PhD in chemistry, materials science, electrical engineering, applied physics, or a related field