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for radiation protection and develops many of the biokinetic and dosimetric models recommended by the International Commission on Radiological Protection (ICRP) and applied by U.S. federal agencies The successful
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data analytics, and manufacturing process optimization. Develop and apply models, algorithms, or data analysis workflows to support machining process understanding, machine tool characterization, process
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AI processes (e.g., model training, inference). Develop agentic AI systems and AI harnessing techniques to enhance model quality, resource optimization, and adaptive execution in diverse workflows
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state-of-the-art high-performance computing. Key Research Areas: AI for Science: Research and development of large-scale AI models for science, focusing on pre-training, instruction-based fine-tuning, and
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Postdoctoral Research Associate to join the Microbial Engineering Group. In this role, you will develop next‑generation genetic tools for non‑model microorganisms, enabling precise genome engineering in
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, etc.) composites. Hands-on experience with lab scale polymer synthesis and analysis. Preferred Qualifications: Experience with computer modelling systems such as finite element analysis (FEA
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theory, thermodynamics, statistical mechanics, or non-equilibrium physics Preferred Qualifications: Rich experience with transport measurements and characterization Basic knowledge of models of strongly
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of Quantum Monte Carlo (QMCPACK, PYQMC) density functional theory (e.g. QE, VASP, PYSCF) and associated models to describe various properties of DOE-relevant quantum materials. The Materials Theory Group has a
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characterization, and predictive fault tolerance in HPC systems. Architectural exploration and performance modeling of high-bandwidth memory (HBM) and DDR memory systems in the context of data-intensive scientific
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modeling, and complementary characterization. We are particularly interested in candidates with backgrounds in optical spectroscopy, scanning probe microscopy, semiconductor materials, or condensed matter