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, chemicals and materials production technologies for their energy consumption, environmental impacts and production cost. Current research efforts include examination of: 1) electro-fuel production technology
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at the APS, integrating x-ray optics and wave propagation models with realistic sample simulations based on dislocation dynamics and molecular dynamics of relevant materials. Significant attention needs
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A postdoctoral position on exascale atomistic simulations, AI/machine learning and data analysis of ferroelectric devices is available immediately at the Center for Nanoscale Materials (CNM
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, Aurora) and one of the brightest synchrotron x-ray sources in the world (APS). Candidates with a background in deep learning, computational physics, computational materials science, inverse problems
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addition to conventional tools such as XRD, BET, XRF, SEM, TEM, etc. Collaborate with computational modeling and artificial intelligence/machine learning teams to improve battery performance, using rational design
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(electrochemistry, materials synthesis, or characterization) or computational simulations perspective, is required. Proficiency in Python programming is required. Familiarity with REST APIs is desirable. Master’s
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, energy, and critical materials supply chains. In this role, you will be responsible for analyzing existing and future supply chains, with a specific focus on understanding the complexities and challenges
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team that focuses on materials for classical microelectronic interfaces and quantum information science. The group actively interacts with the broader Argonne and UChicago community of scientists as
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analysis of (1) decarbonization pathways for energy- and emissions-intensive industries and (2) material circularity pathways in a low-carbon future. The candidate would conduct research across multiple DOE
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory is seeking postdoctoral researchers to work on distributed quantum computing. The project aims to develop superconducting