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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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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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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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applying artificial intelligence (AI) and machine learning (ML) methods for the autonomous, self-driving synthesis of nanoscale and quantum materials. This is an exciting opportunity to help shape a
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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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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