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Postdoctoral Fellow - Materials Chemistry, Texas Materials Institute, Cockrell School of Engineering
at the intersection of robotic synthesis, AI-guided materials discovery, and rapid characterization to enable efficient screening and optimization of functional materials. The Postdoctoral Fellow will collaborate with
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execute experiments that advance the frontier of self-optimizing microscopy, including automated alignment, adaptive focusing, drift correction, and AI-assisted atomic structure recognition. The role
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agentic orchestration frameworks for multi-step, multi-instrument experimental workflows (e.g., observe–reason–plan–act). Design closed-loop optimization and active learning strategies for real-time
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the last three years. Solid experience with AI/machine learning methodologies, particularly those applicable to network optimization. Proven ability in programming and familiarity with network simulation
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, advanced optimization and control of semiconductor manufacturing processes and systems. Experience in working with advanced AI/ML software packages and super-computing cluster systems, including Hadoop
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one or more of the following areas: (1) modeling of infectious disease dynamics, (2) statistics, machine learning, and AI, or (3) operations research and optimization. Preference will be given
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. Responsibilities The research fellow will be engaged in the development of hybrid optimization methods for seismic inversion in anisotropic media and their efficient applications to active and passive source seismic