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of data management practices and AI/ML approaches for experimental planning, workflow optimization, and data analysis is beneficial Advanced ability to interpret and integrate complex experimental results
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, primarily for recycling used nuclear fuel to support the deployment of advanced reactors. The selected candidate will develop and optimize novel separations chemistries to recover actinide and rare earth
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complex instruments and run simulations to accelerate discovery. This involves navigating vast parameter spaces, identifying rare or transient phenomena, and dramatically optimizing the use of precious
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include : Leading the physics design of a next-generation proton linac, optimizing acceleration efficiency and transverse focusing Designing new SRF accelerating cavity types across multiple operating
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, advanced computational techniques, and data science. The project involves: Materials Development Platform that will enable redox molecule optimization via predictive simulations, database management, and AI
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other collaborators for the development and optimization of experimental process; 2) analyze collected data with Argonne’s EverBatt model to estimate associated cost and environmental impacts; 3) write
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, computational scientists, and engineers to identify use cases and validate AI-driven discoveries. Optimize system performance for deployment on high-performance computing infrastructure and cloud platforms
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optimize epitaxial growth of complex oxide nanostructures, especially ferroelectrics, via solid-phase epitaxy (SPE) Perform thin-film and device characterization across structural (XRD, AFM, SEM, XPS, TEM