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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory seeks an outstanding postdoctoral researcher to advance data-driven, physics-informed AI for microelectronics materials
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to cross-border research integration. The role will focus on implementing models, preparing datasets, and supporting prototype delivery under the guidance of the AI Engineering Lead. Please note: The
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Integrated photonics is maturing and integration density increases. This means the design methods and testing strategies need to keep up. Are you interested in exploring compact models and novel
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resides in is to build up technology for data-driven pattern recognition to infer data-driven generalized auditory profiles. The central challenge for this endeavor are the small sample sizes (of typically
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global climate action through modelling and data-driven Antarctic research 3 year fixed-term full-time role based in Hobart with relocation assistance considered About the opportunity We are looking for a
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applications ranging from next-generation laser technology and precision material processing to laser-driven self-assembly and surgery, all the way to a long-term vision of developing an “Atom Printer” based
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levels. The candidate is expected to: Build models embedding dynamics and flexibility of process operations and related supply chains Combine analytical and data-driven surrogate models in optimization
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, data analysis, and modelling, in collaboration with the University of Southampton and the National Physical Laboratory (NPL). With a background in Materials Science, Physics, Chemistry, or Engineering
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
. These frameworks integrate IoT-enabled sensing and real-time analytics to support continuous monitoring, data-driven evaluation, and predictive maintenance. The work also address user comfort and indoor
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, sustainable materials, or engineering to contribute to our ongoing research on improving the hygrothermal performance, structural modeling, and durability of CLT, including hybrid and thermally modified wood