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integrating these results into the ISAAC multimodal data ecosystem to support autonomous scientific discovery. The postdoctoral researcher will contribute to the development of AI-ready data pipelines and
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, both focused on multimodal synchrotron characterization of defects and interfaces in oxides and 2D materials. These positions are part of a cross-facility initiative to build an “AlphaFold
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) applied to scientific problems Strong background in managing multimodal datasets Proven experience collaborating with experimental teams to validate computational models Ability to model Argonne’s core
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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datasets, complex simulations, and multimodal information. This position provides the opportunity to work with some of the world’s most advanced computing resources, including flagship exascale
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Postdoctoral Appointee - Investigation of Electrocatalytic Interfaces with Advanced X-ray Microscopy
maintaining in-situ/operando electrochemical sample environments. Processing and curating data for inclusion in the ISAAC multimodal data framework. Closely collaborate with ISAAC team members to support the
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) for Retrieval-Augmented Generation (RAG). Experience with prompt engineering and chain-of-thought techniques. Experience with multimodal AI and/or foundation models. Job Family Postdoctoral Job Profile
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for this postdoctoral position to work on development and scaling of the data infrastructure and software for AI applications on supercomputing systems and AI testbed systems. The postdoc will work on multimodal data
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simulations, design and conduct experiments, and analyze multimodal data streams in a continuous, real-time loop with minimal human intervention (https://www.nature.com/articles/s41524-024-01423-2 , https
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photonic platforms for hybrid quantum systems. The role offers a unique opportunity to engage in advanced materials synthesis, nanofabrication, and multimodal characterization using Argonne’s world-class