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: Federally sponsored research in predictive intelligent networking Position expected to continue until March 1, 2027. Must be eligible to work in the United States on a full time basis for any employer
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, strengthening ties between current Fellows and a 25-year alumni network committed to public service. Engage previous Archer Fellows and the broader UT Austin alumni community (Texas Exes) to develop strategic
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condition, including technical research as needed Dialog with curators and collection managers about treatment choices Carry out minor and complex single-item treatment and housing for collection items
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Postdoctoral Fellow - Materials Chemistry, Texas Materials Institute, Cockrell School of Engineering
to the rapid exploration of novel materials chemistries—such as compositionally complex energy materials—using high-throughput, combinatorial synthesis strategies. The position involves working
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microscopy systems that integrate machine learning, robotic control, and real-time data analysis to achieve autonomous imaging and interpretation of complex materials systems. The Fellow will design and
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-time data analysis, and tool-use APIs to automate complex decision-making across materials design, liquid-phase synthesis, and characterization platforms. Responsibilities include building agentic AI
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convolutional neural networks (CNNs), generative AI methods such as diffusion models, and interpretability techniques commonly applied in hydrology including SHAP or LIME for explaining outputs of forecasting