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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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into APS beamlines and enabling transformative experiments in X-ray spectroscopy. This is an opportunity to contribute to a growing capability at one of the world’s premier synchrotron facilities
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the application of these methods and tools to complex issues in the domains of environmental, water, and energy system analysis. Position Requirements Recent or soon-to-be completed (typically within the last 0-5
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to contribute to cutting-edge research in catalysis, with a focus mechanistic investigation of surface effects in non-traditional catalyst systems In this role you will • Conduct research as part of a
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magnetic thin films, patterned structures, and complex interfaces. In this advertised role, you will be conducting real-space imaging of magnetic heterostructures using LTEM to understand spin textures and
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discipline • A strong experimental background in transmission electron microscopy • Experience with focused ion beam preparation of electron microscopy specimens • Experience with analysis of complex images
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) in field of organic, organometallic, or inorganic chemistry Expertise in the synthesis and characterization of MOFs and air-sensitive transition metal complexes Experience in conducting kinetic and
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Laboratory seeks a postdoctoral appointee to join a multidisciplinary team developing complex systems models, including agent-based models, and new algorithms and tools for machine learning and optimization
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organometallic / inorganic heterogeneous catalysis Design, synthesize, and characterize metal-ligand complexes supported on metal oxide and/or non-traditional support materials Investigate the catalytic activity
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heterogeneous Catalysis focusing on stimulus driven and non-equilibrium catalysis Design, synthesize, and characterize well-defined complexes supported on metal oxide or non-traditional support materials