40 pattern-recognition "https:" "CMU Portugal Program FCT" Postdoctoral positions at Argonne
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The High Energy Physics Division at Argonne National Laboratory invites applications for a postdoctoral appointment focused on the design and simulation of advanced detectors for future high-energy
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In 2014, our team developed a compact proton linear accelerator (linac) design for an accelerator-driven system (ADS) aimed at nuclear waste transmutation, based on state-of-the-art niobium
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design and build custom, non-commercial apparatus compatible with synchrotron scattering and imaging techniques at the Advanced Photon Source. Candidates with prior experience in developing operando
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. Advanced materials design and characterization will be pursued through various strategies. The candidate should have extensive experience in fabricating and testing prototype batteries, glove box operation
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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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quantum transduction and terahertz (THz) photon generation via enhanced light–matter interactions. The postdoc will lead efforts in device patterning and the integration of complex materials—such as
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design, develop, and evaluate AI-driven scientific visualization assistants that support intuitive, context-aware interaction with large-scale simulation and experimental data. The postdoc will focus
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optimization of hydropower systems and their integration with broader electricity markets. Conduct research on electricity system operations, planning, and market design, including evaluation of economic and
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to assess evolving risks in coastal-urban regions. Other key responsibilities include: Mesh design and high-resolution data utilization. Develop and refine high-resolution barotropic ocean meshes along U.S
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for the conceptual framework, design, and implementation of these machine learning models, ensuring trustworthy computations and scalability on the DOE’s leadership computing facilities. The focus will be