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Develop and apply operando diagnostic tools for aqueous battery research Design, perform, and analyze electrochemical and materials characterization experiments Use imaging and sensing techniques, including
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) at Argonne National Laboratory to advance learning-enabled imaging methods. This position offers a unique opportunity for candidates with backgrounds in electrical engineering, computer science, applied
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. Experience applying computer vision, image analysis, and/or machine-learning methods to microscopy or materials characterization data. Demonstrated ability to analyze microstructural data and relate
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imaging, ideally with the ability to carry out advanced measurements at the Advanced Photon Source. Experience with in-situ characterization and experimental apparatus development is especially valued. In
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simulations on the Aurora supercomputer, using AMReX (https://amrex-codes.github.io/amrex/ ) and the lattice Boltzmann method (LBM). The candidate will develop flow/geometry-aware refinement strategies that go
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technology research and development by fabricating prototype electrodes and pouch cells in a dry-room environment and by contributing to experimental design, data interpretation, and technical reporting
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across heterogeneous data types such as clinical, imaging, omics, text, and experimental data. The work will include developing approaches for continual model improvement, adaptive federated training
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implementing BCDI, XPCS, nano-beam and time resolved microscopy, PDF, 3D imaging techniques, PCI, and other techniques for advanced characterization of materials across solid-liquid and into melt. Measurements
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developing advanced AI/ML models for applications in physics, chemistry, or materials science Experience with periodic simulation codes such as VASP Proficiency in Python programming Excellent written and oral
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using computing clusters for simulation and data analysis. Experience with accelerator modeling and simulation codes, such as Elegant and Cheetah. Experience with SDDS, Tcl/Tk, and scripting in Linux