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The Chemical Sciences and Engineering Division at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct innovative research focused on the synthesis, recycling, and performance
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, including new solvents, additives, and salts, that offer enhanced thermodynamic and/or kinetic stability at electrode/electrolyte interfaces. The ultimate objective is to enable long-term cycling performance
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis
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for holotomography and to perform dynamic experiments using the Projection X-ray Microscope (PXM) instrument for studying microelectronics. As part of a collaborative team, the successful candidate will participate in
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be to develop high fidelity simulations and/or algorithms to enable Bragg coherent diDraction imaging. We expect x-ray ptychography and coded aperture methods to play a fundamental role in creating a
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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
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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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performs world-leading research in nuclear structure, nuclear astrophysics, fundamental symmetries, and nuclear data. The Group also manages and operates world-class detector systems as part of the ATLAS