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: Proficiency in machine learning, statistical modeling, and quantitative methods for multi-omics data analysis Molecular Simulations: Expertise with molecular simulation tools like OpenMM, AMBER, Gromacs, and
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data to guide intelligent data processing strategies and inform detector and readout device design Work collaboratively within a cross-disciplinary team and contribute to publications and presentations
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The Data Science and Learning Division (DSL) at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting edge molecular and microbiology work to enhance non-proliferation
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will receive full consideration. Key Responsibilities AI-ready data and analysis for the ePIC Barrel Imaging Calorimeter and our Jefferson Lab program Support for the PRad-II and X17 experiments
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-completed PhD with strong background in Materials Science or Physics (within the last 5 years) Considerable experience in understanding magnetic-domain physics in thin film and/or nanostructured materials
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fabricate nanoscale electrical test structures (e.g., photolithography, e-beam lithography) Design, test, and characterize radiofrequency (RF) circuitry and measurement approaches Analyze and interpret data
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-completed PhD (typically completed within the last 0-5 years) in chemical engineering, environmental engineering, or similar degree. Experience with data collection, processing, analysis, and presentation
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spectroscopy (e.g., transient absorption), including laser operation, optical alignment, and data analysis Experience in synthetic inorganic chemistry and transition metal complex photophysics Job Family
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well as university partners on coastal methods and validation strategies. Mentor summer students on data analysis and visualization workflows. Publish in peer-reviewed journals, present at scientific conferences, and
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. Design, implement, and validate experimental setups; conduct synchrotron-based measurements on quantum and energy materials. Build robust data reduction and PDF analysis workflows; document best practices