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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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the development of AI models for robotic control and the demonstration of these methods via simulation and experiment. Beyond the listed projects, the candidate may contribute to other large-team scientific
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growth, electricity usage, and their implications for U.S. supply chains and energy infrastructure plans. The successful candidate will apply methods from economics, supply chain risk analysis, and data
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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
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and contributing to reusable research software when appropriate. Position Requirements Required skills, experience and qualifications: PhD in computer science, applied mathematics, electrical
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synchrotrons and x-ray free-electron lasers. Key Responsibilities Perform electronic-structure calculations using ab initio quantum chemistry methods and software (commonly CASSCF-based approaches) Investigate a
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, co-simulation, or hardware-relevant environments. Primary Responsibilities: Develop machine learning and AI methods for control, optimization, and cyber-resilient operation of distribution systems, DER
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in computational science, machine learning, and experience with synchrotron data analysis are strongly encouraged to apply. Position Requirements PhD completed in the past 5 years or soon to be
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Plan and execute in situ/operando experiments using advanced characterization methods, including Near Ambient Pressure X-ray Photoelectron Spectroscopy (NAP-XPS), electron microscopy, Raman spectroscopy
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, lithium-ion batteries, etc.), evaluating the competitiveness of mining projects, and developing economic models that support DOE priorities. The successful candidate will apply methods from economics