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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 candidate is expected to lead an effort to prepare generalized ML techniques for data quality monitoring for tasks across multiple HEP experiments. Experiments with Argonne involvement include, but are not
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to ensure quality data. Communicate effectively with supervisors, peers, and Laboratory management through status updates, technical research reports, project presentations, and other regular channels
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related field Strong molecular biology skills (cloning, vector design, transformation), protein and nucleic acid prep-scale purification and analysis, and quantitative data analysis Excellent communication
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory seeks an outstanding postdoctoral researcher to advance data-driven, physics-informed AI for microelectronics materials
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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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, present at conferences, and contribute to data sets and code repositories. Position Requirements Required Skills, Knowledge and Experience: Ph.D. (received within the last 0–5 years or by start date) in
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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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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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. - Experience with automation and computer interfacing - Experience with advanced data analysis implemented in languages such as python - Direct research experience in quantum material systems used for quantum