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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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numerical algorithms and large parameter scans. Experience using high-performance computing clusters for simulation and data analysis. Experience with accelerator modeling and simulation tools such as
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these fforts. A successful candidate will collaborate with scientists in DIS, Environmental Sciences division and Argonne leadership Computing Facility to further develop the AI foundation models
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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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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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and implementing micromechanical models, ideally including crystal plasticity methods. Good grasp of numerical methods for implementing and solving complex physics-based models in the context of the
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behavior of soft materials under operating conditions. The successful candidate will implement state of the art molecular dynamic simulations using machine learning potentials and apply free energy methods
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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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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory invites applications for a postdoctoral research position focused on developing AI/ML methods for autonomous materials
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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