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The Data Science Learning Division at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting-edge computational and systems biology research. The primary focus
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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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. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in mechanical engineering, materials science, civil engineering, structural engineering, or a closely
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. Position Requirements A formal education in Physics, Materials Science, Chemistry, or a related field at the PhD level with zero to five years of employment experience. Demonstrated experience with high
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and technologies, and in advancing data-driven risk monitoring approaches for supply chain resilience. The candidate will conduct comprehensive supply chain mapping, modeling, and analysis—integrating
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Requirements Required skills, abilities, and knowledge: Recent or soon-to-be completed PhD (within the last 0-5 years) by the start of the appointment in computer science, electrical engineering, applied
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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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facilities with relevance to critical materials and nuclear reprocessing Analyze data, prepare manuscripts for submission to peer-reviewed publications, prepare technical reports for sponsors, and attend and
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on the development of the SPHEREx Legacy Galaxy Clusters Catalog. The successful candidate will lead analyses to characterize galaxy populations in clusters using SPHEREx data in combination with complementary wide
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, reproducibility, and scalable data understanding Position Requirements PhD completed within the last 0–5 years (or near completion) in Computer Science, Computational Science, Visualization, Human–Computer