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optimization schemes. From developing AI models to uncover structure-function relationships with limited data sets, to building automated electrode-electrolyte interface discovery workflows and implementing full
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of dynamical systems, which will be integrated into large-scale optimization frameworks to enhance the efficiency and reliability of power grid operations. The Postdoctoral Appointee will be responsible
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evaluate well-defined nanomaterials for catalytic and electrochemical applications Develop, implement, and optimize in situ electrochemical cells to probe liquid–solid interfaces under reaction conditions
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presence at the Argonne campus in Lemont, Illinois. Preferred Qualifications: Experience in interdisciplinary collaborative research. Knowledge of large scientific code management and optimization. Job
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Mathematics, or a closely related field. Design and optimize multimodal LLMs to encode, fuse, and reason over heterogeneous scientific data from diverse modalities such as numerical tables, text, and images
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terms of universal computational skills. We encourage you to describe your work using broader technical terms—such as "statistical data analysis," "workflow automation," or "algorithm optimization"—rather
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(CFD) to develop and optimize new processes and equipment designs using high-performance computing Develop process- and facility-scale models as the foundation for digital twins of chemical processing
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, including synthesis of fuel materials and recycling of used MSR salts. The selected candidate will develop and optimize process chemistries to synthesize chloride and fluoride species, recover metals and
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ecosystem. Key Responsibilities Develop and optimize lithographic patterning of nano- and meso-scale structures, such as gratings, waveguides, cavities, and metamaterials for quantum and THz devices Integrate
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of Cyanobacteria and carrying out experiments with those bacteria, as well as flow cytometric analysis. Key Responsibilities: Develop and optimize extraction methods for recovering ultra-long, high molecular weight