72 phd-position-computer-science-"IMPRS-ML"-"IMPRS-ML" positions at King Abdullah University of Science and Technology in Saudi Arabia
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We invite applications for a faculty position in chemical engineering with a focus on process modeling, control, and optimization, as well as data-driven methods for sustainable energy and advanced
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We invite applications for a faculty position in computational science and engineering with a focus on geophysics or fluid dynamics, as well as machine learning with one of the following experiences
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We are currently seeking exceptional candidates to join our faculty in the Physical Sciences and Engineering (PSE) Division to lead pioneering research in polymer chemistry, particularly in
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The Head of the KGSP oversees Saudi Arabia's most important STEM scholarship for the nation's highest-performing students. The KGSP is not only a crucial strategic source of Saudi talent for KAUST but also the strongest pipeline of talented scientists, innovators, and thought leaders for Saudi...
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Education: Ph.D. or M.S. in Computer Science, AI, Computer Vision, or related field Experience: 3+ years in computer vision and deep learning, with specific focus on microscopic imaging, generation
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(KAUST) invites applications for a faculty position at the rank of Assistant Professor (exceptional candidates at the Associate Professor level may be considered). The Statistics (STAT) Program at KAUST
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The Bioscience Program in the Biological and Environmental Science and Engineering (BESE) Division at King Abdullah University of Science and Technology (KAUST) is launching a significant initiative
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· PhD in Materials science, chemistry, physics, polymer science, or related field · Strong background in ferroelectrets, piezoelectric materials, voided charged polymers or piezocomposites
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We are currently seeking outstanding candidates to join our faculty team within the Physical Science and Engineering Division (PSE) to lead pioneering research in: - Carbon Fiber
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to multi-omics single cell data. The 3 positions require skills (each in different degrees and balance) on high-performance computing, dynamical systems, machine learning techniques for high-dimensionality