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Modeling naturally fractured reservoirs is re-gaining interest in the Oil & Gas industry and academia for application in carbonate fractured reservoirs and unconventional reservoirs where natural
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We seek a postdoctoral associate with PhD degree in Physics, Electrical Engineering, Materials Science or in related fields specialized in acoustic sensors, device fabrication, and electromechanical
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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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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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and algorithms for efficient learning of dynamical models of micro-states evolving over time including machine based learning of derivation of coarse grained representations; (ii) implementation
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generous funding for research, a cutting-edge genomics facility, and top-tier high-performance computing infrastructure, fostering an excellent environment for genomics-based research. The position is fully
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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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for chemical and morphological characterization. The candidate should preferentially have a PhD in chemistry, polymer science, or membrane preparation and characterization. Besides the salary (contract of 2
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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