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-informed AI framework that decodes the complex relationships between material defects, functional fields (e.g., strain, electrostatic potential), and device performance, with a primary focus on leveraging
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machine learning models at a world-class high-performance computing facility The candidate will have access to state-of-the-art computing resources, including: NVIDIA DGX-2 Systems: Powerful platforms
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multidisciplinary team, the candidate will work at the intersection of AI/ML, domain sciences, and high-performance computing. The role requires a strong foundation in LLMs and machine learning, along with
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mechanisms of photoinduced charge transfer and spin state conversion across a wide spectrum of systems, including small transition metal complexes and donor-acceptor systems. By performing state-of-the-art
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multidisciplinary team of scientists and High Performance Computing (HPC) engineers. In the AL/ML group, we work at the forefront of HPC to push scientific boundaries, carrying out research and development in state
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In 2014, our team developed a compact proton linear accelerator (linac) design for an accelerator-driven system (ADS) aimed at nuclear waste transmutation, based on state-of-the-art niobium
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primary goal of this work is aimed at advancing next-generation, lithium-ion technology through a detailed understanding and mitigation of surface degradation mechanisms that limit state-of-the-art lithium
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Deploy, commission, and operate TES X-ray microcalorimeter spectrometers at APS beamlines, including cryostat integration, beamline interfaces, alignment, and stability optimization. Perform pathfinding
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have access to state-of-the-art research facilities and gain in-depth knowledge of the research frontiers of in situ characterization of thin film deposition, heterogenous interfaces, and autonomous
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. Working within the Center for Nanoscale Materials (CNM), the successful candidate will leverage state-of-the-art functional and dynamic TEM capabilities to lead experimental research on defect species