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performance of materials under extreme conditions. The candidate will work with a multidisciplinary team to develop models in the MOOSE multiphysics finite element framework. These models will include
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capabilities in federated learning frameworks, with emphasis on scalable, reproducible, secure, and extensible research software. Evaluate model performance, robustness, generalizability, fairness, privacy, and
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laboratory workflows. The position will focus on building the data resources, predictive models, and closed-loop decision frameworks needed to accelerate experimentation and advance next-generation autonomous
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expertise in machine learning, computational imaging, computer vision, or signal processing. Proficiency in scientific programming and modern ML frameworks, with the ability to implement and debug research
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bonding, defects, catalysis, batteries, solid-state chemistry, molecular systems, or related materials classes. Strong Python skills and familiarity with LLM APIs, agent frameworks , PyTorch, and the Python
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fluorescence tomography at beamline 2-ID-E and 2-ID-D with focus on bioimaging of soil aggregates, as well as computational framework for modeling and reconstruction of related 3D datasets. The successful
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, particularly for high temperature components. Experience with the MOOSE finite element simulation framework. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time
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workflows, and immersive or experimental interfaces Integrate LLM-based and agentic AI systems with scientific visualization frameworks, in situ pipelines, and data analysis workflows Prototype and evaluate
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techniques to industrial energy systems, manufacturing systems, and commodity supply chains. Integrate data-driven methods with optimization-based modeling frameworks, including linear, mixed-integer
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for Microelectronics” —a physics-informed AI framework that links composition, structure, and operating conditions to defect evolution and functional performance. The successful candidates will lead experimental