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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 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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. - 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
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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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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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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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experiments and corresponding data analysis. Following the successful demonstration of the technique, the candidate will collaborate with team members from material science to apply these methods to scientific
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and implement power grid planning/operations algorithms and tools and conducts data analysis related to energy and power systems, with emphasis on the following areas: Variable energy resource
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, MATLAB, or similar programming environments for instrument control and data analysis. Excellent written and oral communication skills. Demonstrated ability to work both independently and collaboratively in
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Hands-on experience with two-dimensional materials modeling Proficiency in database development and management for computational materials data Strong programming skills and experience with software