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are in particular targeting development of data-driven high-performance computing techniques for unbiased discovery of generative models & theory and algorithms for network inference with special reference
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in simulation, prediction, and analysis of large-scale and complex fluid systems. Special emphasis will be directed toward incorporating high-performance computing, advanced algorithms, machine
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industry partners. Design, implement, and validate advanced reinforcement learning models. Utilize reinforcement learning and evolutionary algorithms to discover new chemical materials. Publish and present
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algorithmic advances and aim at publishing them at these top venues. More details about the position can be found here. As a KAUST postdoc or researcher, you will have access to state-of-the-art research