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applied research on AI-driven and AI-enhanced industrial energy systems optimization modeling, material flow analysis, and supply chain analysis of industrial commodities and critical materials
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analysis frameworks that enable rapid interpretation of scattering measurements and facilitate the training of intelligent agents capable of guiding experiments and simulations in catalytic materials
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financial models. The position will include the analysis of hydropower operation and expansion, optimization and equilibrium, market penetration, and interdependencies. This description documents the general
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
The Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invites outstanding candidates to apply for a postdoctoral position in the area of uncertainty quantification and
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venues Position Requirements Required skills and qualifications: A PhD degree completed within the last 0-5 years (or soon to be completed) in numerical analysis, applied mathematics, computational science
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extraction and analysis of sequencing-based omics data. Experience in bioreactor configuration, inline/online sensor integration, and PID-based process control; familiarity with bioreactor automation systems
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undergraduates. Postdocs benefit from strong interactions with experts in applied mathematics, computer science, device physics, materials science, and statistics, as well as access to world-leading supercomputing
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candidate is also expected to aid in the design, analysis, and interpretation of experiments as well as present findings to the community through publications and presentations. Position Requirements Recent
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at conferences and ALCF/DOE venues. Position Requirements Required Skills and Qualifications: Ph.D. in Computer Science, Physics, Chemistry, Biology, Engineering, Mathematics, or a related computational discipline
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of modern AI tools to accelerate the entire research workflow—including literature exploration, experiment design, implementation, analysis, and dissemination. The researcher will work in a collaborative