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(e.g. weather, hydrology). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative
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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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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 solver, capable of running
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, or network-based modeling of infrastructure or industrial systems. Familiarity with high-performance computing, cloud computing, or parallel computing environments for training models and solving optimization