51 web-programmer-developer-university-of-liverpool Postdoctoral positions at Argonne
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The Chemical and Fuel Cycle Technologies division at Argonne is seeking a Postdoctoral Appointee to join a multidisciplinary team developing molten salt-based chemical and electrochemical processes
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-disciplinary analysis efforts, and shape a growing research program. The successful candidate will receive strong mentorship and autonomy to develop a scientific vision, build collaborations, and pursue high
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candidate will work on cutting-edge research integrating genome-scale language models (GenSLMs) with deep mutational scanning data, and experimental virology to predict viral evolution and identify emerging
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with other Argonne divisions, national laboratories, universities and industry partners. Position Requirements Knowledge in the following four areas : 1. Modern methods for developing large-scale agent
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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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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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readout and controls (e.g., SQUID-based time- or microwave-multiplexed systems) with beamline data acquisition and control (EPICS/Bluesky). Develop and maintain data acquisition, calibration, and analysis
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contribute to research and model development to enhance the resilience of domestic and global supply chains for clean energy technologies. Lead technical and policy analysis to inform decision-makers
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reaction pathways that have potential impact on aqueous pollution remediation. Deeper insights into water-solid interfaces are essential for development of innovative and efficient technologies to extract
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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors