61 parallel-computing-numerical-methods "Simons Foundation" Postdoctoral positions at Argonne
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-Informed Neural Networks (PINNs) and geometric deep learning. Experience with active learning, agentic workflows, or other methods for autonomous experimentation. Familiarity with high-performance computing
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the past five years or soon-to-be completed in physics, materials science, chemistry, engineering, or a related discipline. Demonstrated expertise in one or more synchrotron X-ray methods such as BCDI, XPCS
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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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growth, electricity usage, and their implications for U.S. supply chains and energy infrastructure plans. The successful candidate will apply methods from economics, supply chain risk analysis, and data
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, distributions, and dynamics in metallic, oxide, and semiconducting systems. This project integrates high-throughput and in situ TEM experimentation with AI/ML-driven image analysis and computational modeling
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processes and develop models of material interactions and behavior in molten salt environments. Develop novel and improved methods for measurements of molten salt properties and standardizing procedures
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of advanced scanning/transmission electron microscopy (S/TEM) methods for cutting-edge scientific research in areas such as quantum materials and low-dimensional energy systems. This position emphasizes
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techniques and analysis of large, multimodal datasets Experience in materials synthesis and materials characterization Strong foundation in general analytical methods and laboratory practices Excellent oral
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laboratory partners, and contribute to the development of separation technologies for energy, water, and critical resources. Key Responsibilities: Develop and apply in-situ methods (e.g., optical coherence
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), and cell free methods. Key Responsibilities: Development and optimization of vector constructs and expression condition characterization of protein yields and quality, and large-scale protein production