10 high-performance-computing-postdoc Postdoctoral positions at Argonne in computer-science
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camera technology and multiplexed readout systems for quantum information science applications. In this role, you will join a multidisciplinary team spanning several Argonne divisions and contribute
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The Chemical Sciences and Engineering Division invites applications for a Postdoctoral Appointee to contribute to innovative research at the intersection of computational catalysis, quantum
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ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experiment, and who is motivated to translate computational advances into real
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emphasize processing–microstructure–property-performance relationships under temperature, irradiation, corrosion, mechanical stress conditions. The successful candidate will integrate additive manufacturing
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processing approaches and dry head-end separations methods; evaluating process performance; and contributing to technical reports, publications, and sponsor deliverables. The successful candidate will work
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needs. As a part of this team, you will : Develop, build and test equipment and perform molten salt separations which support the development of a secure fuel supply for MSRs and nuclear fuel cycle
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assess vehicle technologies to quantify energy consumption, performance and cost benefits. In this role, a successful candidate will perform vehicle modelling and simulation of advanced powertrains
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platform for X-ray absorption spectroscopy by integrating LLMs, scientific machine learning, physics-aware workflows, and strong computational chemistry/electronic-structure expertise. The researcher will
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of hydrometallurgical processes such as leaching, solvent (liquid-liquid) extraction, and adsorption; evaluating process performance and operability; developing test plans and standard operating procedures; assisting
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models for high-temperature structural materials with applications in nuclear reactors and other energy systems. The candidate will collaborate with ANL staff to review, validate, and enhance methods