79 high-performance-quantum-computing-"https:"-"https:" Postdoctoral positions at Argonne
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, and use of novel architectural features. Argonne National Laboratory is a multi-disciplinary research institution offering world-class opportunities in High-Performance Computing and housing the Argonne
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/reactions, with increasing emphasis on using artificial intelligence and quantum information science. The group has access to extensive laboratory and national computational resources and has significant
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, collaborate with detector scientists and beamline teams, and lead forefront experiments that leverage the sub-eV to few-eV energy resolution and high quantum efficiency of TES arrays. Key Responsibilities
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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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design, advanced modeling and high-performance computing, mathematics and data analytics, AI/ML algorithm development, and accelerator operations Ability to model Argonne’s core values of impact, safety
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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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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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This is an opportunity for a knowledgeable and creative individual to be part of a team using artificial intelligence and high-performance computing to evaluate the state of health (SOH
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define requirements and performance specifications for future HEP/NP detector systems Perform detector concept development, system-level design, and optimization leveraging emerging computing architectures
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. Design, implement, and validate experimental setups; conduct synchrotron-based measurements on quantum and energy materials. Build robust data reduction and PDF analysis workflows; document best practices