11 computational-physics 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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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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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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skills. The Physical Science and Engineering Division (PSE) at KAUST encompasses five Academic Programs: Materials Science and Applied Physics, Earth Systems Science and Engineering, Chemistry, Chemical
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The Division of Physical Science and Engineering at King Abdullah University of Science and Technology (KAUST), Saudi Arabia, invites applications for Postdoctoral fellow in Mechanical Engineering
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opportunity to leverage KAUST’s unparalleled access to the Red Sea, serving as a catalyst for research that uses computational approaches to integrate biological, physical, and chemical data within a cohesive
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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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The Bioengineering (BioE) Program in the Biological and Environmental Science and Engineering (BESE) Division at King Abdullah University of Science and Technology (KAUST) bridges the University’s
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holding a PhD in chemical, environmental or process engineering, to apply for a full-time post-doctoral fellowship position in the field of water desalination, focusing on the development of an artificial
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-inspired approaches for modeling, designing, and predicting the response of composite systems. Responsibilities: Develop AI approaches for predictive multi-physics response of composites in Energy