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agencies as well as companies to help move the nation toward an economy based on reliable energy. The successful postdoctoral candidate will join a team of Argonne researchers and work closely with federal
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, modeling and analysis, integrating diverse data sets to identify global risks affecting sourcing strategies. In this role you will: Conduct and contribute to research and model development to enhance
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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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candidate will work on cutting-edge research integrating genome-scale language models (GenSLMs) with deep mutational scanning data, and experimental virology to predict viral evolution and identify emerging
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in high-voltage battery systems through a fundamental understanding of interfacial mechanisms. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in Organic Chemistry
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of the protein expression platforms, currently utilizing E. coli and mammalian cell lines, to onboard other microbial systems (e.g., fungi), insect-cell (baculovirus) systems, plant-based expression (in planta
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are seeking an postdoctoral appointee to contribute to this research to understand the underlying physics of spin and charge based memory materials using advanced in-situ transmission electron microscopy (TEM
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Energy Systems and Infrastructure Analysis Division. We are seeking applicants with a strong technical background and expertise in international trade modeling, particularly in the upstream automotive
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, and optimize for energy efficiency HPC applications and high performance data stream analytics workloads. Use of novel accelerator designs, and automatic methods to model/predict how performance would
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and datasets • Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term