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, synthesize, and process information to develop high-quality datasets and derive empirically driven results; work with other team members to develop and apply cutting-edge methodologies to address critical
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for developing new computational tools and AI/ML approaches to analyze and correlate data from multiple imaging modalities, including synchrotron tomography, x-ray fluorescence microscopy, visible light microscopy
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A postdoctoral position on exascale atomistic simulations, AI/machine learning and data analysis of ferroelectric devices is available immediately at the Center for Nanoscale Materials (CNM
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, Safety, Health, and Quality Assurance standards and policies Process and interpret experimental data; prepare manuscripts for peer-reviewed publications Present research findings at scientific conferences
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deployment, monitoring, and optimization of complex scientific data streaming workflows for current and future production infrastructures. The project will involve close collaboration with a team of systems
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, and analyze scientific data specifically related to climate risks, including extreme weather events, long-term environmental changes, and their impact on infrastructure and ecosystems. This role
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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
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equipped with world-leading full-field imaging instruments, including ultrahigh-speed imaging. The group also develops end-to-end scientific software, data analysis, and interpretation methods
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following chemical engineering principles, considering both plant economics and emission reduction (relative to incumbent industrial process). The candidate will extract energy and emissions data associated
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We are seeking a highly motivated Postdoctoral Researcher with expertise in computational biology, deep mutational scanning data, and generative artificial intelligence (AI). The successful