11 coding-"https:" "https:" "https:" "https:" "https:" "https:" "Data driven Materials Modeling" positions at Argonne
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: https://rpl.cels.anl.gov/ Autonomous Discovery at Argonne: https://www.anl.gov/autonomous-discovery MADSci on GitHub: https://github.com/AD-SDL/MADSci AD-SDL organization on GitHub: https://github.com/AD
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Venkat Srinivasan, Director of the Argonne Collaborative Center for Energy Storage Science: [email protected] More information on ESRA and the research areas of focus can be found at: https
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Publications: 1. P. Chen et al., Ultrafast photonic micro-systems to manipulate hard X-rays at 300 picoseconds, Nat Commun, 10:1158 (2019). https://doi.org/10.1038/s41467-019-09077-1. 2. P. Chen et al., Optics
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learn from experiments at unprecedented scale. For more information: Rapid Prototyping Lab: https://rpl.cels.anl.gov/ Autonomous Discovery at Argonne: https://www.anl.gov/autonomous-discovery MADSci
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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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for energy and quantum technology applications. The research encompasses the development of methods and codes to study the interaction between materials and light, and the study of specific materials, in
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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
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related field. Experience with finite element simulations and developing constitutive models. Knowledge of high temperature creep crack growth. Knowledge of engineering design codes such as the ASME Boiler
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with DFT or electronic-structure codes such as VASP, Quantum ESPRESSO, CP2K, ABINIT, GPAW, Gaussian, ORCA, Q-Chem, or related packages. Strong materials science or chemistry domain knowledge, such as