86 high-performance-quantum-computing-"https:"-"https:"-"https:"-"https:"-"https:" positions at Argonne in United States
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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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of advanced scanning/transmission electron microscopy (S/TEM) methods for cutting-edge scientific research in areas such as quantum materials and low-dimensional energy systems. This position emphasizes
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++, or similar languages. Demonstrated expertise in machine learning, especially in the context of dynamical systems modeled by differential-algebraic equations. Experience with high-performance computing and the
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proficient in performing chemical reactions under high-temperature and high-pressure conditions incorporating various approaches including catalytic processes, oxidizing/reducing environments, different gases
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multidisciplinary team, the candidate will work at the intersection of AI/ML, domain sciences, and high-performance computing. The role requires a strong foundation in LLMs and machine learning, along with
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-the-loop exploration of extreme-scale scientific data. This position sits at the intersection of scientific visualization, agentic AI systems, human–computer interaction (HCI), and high-performance computing
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scientists, roboticists. The project will focus on developing an integrated autonomous lab system for strucutre-property characterization of novel materials heterostructures for quantum and microelectronics
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for Microelectronics” —a physics-informed AI framework that links composition, structure, and operating conditions to defect evolution and functional performance. The successful candidates will lead experimental
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. Extensive hands-on experience with analytical characterization of bioprocess samples, including high-performance liquid chromatography (HPLC), gas chromatography (GC), inductively coupled plasma optical
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Argonne National Laboratory seeks a postdoctoral researcher to help build a high-resolution coastal-urban flooding modeling capability within the Energy Exascale Earth System Model (E3SM