7 modelling-complexity-geocomputation Fellowship positions at King Abdullah University of Science and Technology
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integrate complex flow on Discrete Fracture Networks (DFN). The objective of this project is to develop a tool to generate DFN models amenable for multiphase flow, and scale up the model to be usable with
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The lab of professor Jesper Tegnér at KAUST has openings for three postdoctoral fellowships in Data-driven Machine Learning for unbiased Discovery of Generative Models with special reference
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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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themes: (a) learning efficiency, computational creativity (zero, few-shot, and long-tail learning of 2D and 3D vision tasks. This also includes efficient generative models that are capable of generating
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or more of the following areas: Controlled, living polymerization for synthesizing polymers with complex architectures (such as block, star, and dendritic polymers) and characterization of their microphase
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feasibility studies of CCS using mechanistic modeling and simulation including uncertainty analysis and risk assessment. Applications are sought for a 1-to-2-year postdoctoral fellow position. The position
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depends on the background of a suitable candidate. The main topics of the group in the past few years were generative modeling, 3D reconstruction, image-editing, and deep learning using 3D data. More