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for new physics and performance studies. Candidates with experience in modern AI/ML methods—such as transformer architectures, tokenization strategies, and embeddings—are especially encouraged to apply
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-Informed Neural Networks (PINNs) and geometric deep learning. Experience with active learning, agentic workflows, or other methods for autonomous experimentation. Familiarity with high-performance computing
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of molten salt chemistry and electrochemistry Develop novel process monitoring and control technologies applicable to molten salt and liquid metal systems Develop advanced molten salt flow systems to enable
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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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We are seeking a highly motivated and flexible postdoctoral researcher to join the Applied Materials Division (AMD) at Argonne National Laboratory to develop advanced methods for in situ and
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field Match at least one of the following profiles: Experience with modern AI methods for nuclear physics or high-energy physics detector data; or Hands-on work with PMTs, SiPMs, or MCP-PMTs (test-beam
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in detection and analysis of rare noble gas isotopes through Atom Trap Trace Analysis (ATTA), an efficient and selective atom counting method based on laser cooling and trapping. This position will
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automated TEM specimen preparation, including focused ion beam (FIB) methods Curate and prepare datasets for AI model training, validation, and inference Disseminate findings through high-impact publications
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multidisciplinary team in heterogeneous Catalysis focusing on non-traditional catalysts and non-equilibrium catalysis • Design, synthesize, and characterize well-defined complexes supported on metal oxide or non
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Electrochemical Mass Spectroscopy (DEMS), and complementary methods Perform electrochemical testing and benchmarking; analyze and interpret complex datasets to elucidate catalytic mechanisms and structure–property