37 parallel-computing-numerical-methods "Fields Institute" Postdoctoral positions at Argonne
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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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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
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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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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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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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morphological change, dendrite growth, side reactions, delamination, and related behavior in hard and soft materials. A strong emphasis will be placed on the integration of AI methods and agentic
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AI/ML methods for analysis, tuning, and control of particle beams and accelerator systems, with applications to the APS injector and the newly-upgraded APS storage ring. This work will explore
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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