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postdoctoral appointee to develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel
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flow and turbulent combustion applications, as well as parallel scientific computing. Knowledge of deep machine learning (using TensorFlow, PyTorch, etc.) and agentic AI for multi-fidelity modeling
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systems. In parallel, they will design and develop agentic AI and physics-aware AI models to accelerate discovery and deepen mechanistic insight in catalysis. This work will be carried out in close
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. This is an exciting opportunity to work within a collaborative group with deep expertise in silicon detectors, Trigger/DAQ (TDAQ) systems, software, and computing. The Argonne ATLAS group plays a
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environment that brings together computer scientists, AI researchers, domain scientists, software engineers, and high-performance computing experts. You will help design and implement new methods for
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UNIX environment, scientific computing languages such as python or C. Demonstrated background in computational modeling or numerical simulation for science. Ability to work within a multi-lab
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accelerator physics, beam dynamics, or free-electron-laser science. Experience with machine learning, surrogate modeling, optimization, or AI-enabled scientific computing. Experience working with complex
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complex algorithms. Experience 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
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closing this gap. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary
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winds, land–atmosphere interactions, or aerosol–cloud interactions Strong experience in numerical modeling and high-performance computing • Experience applying AI/ML methods to model development, with