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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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artificial intelligence/machine learning (AI/ML). The successful candidate will contribute to the group’s broad physics program, which includes precision Higgs and Standard Model measurements, and searches
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growth, electricity usage, and their implications for U.S. supply chains and energy infrastructure plans. The successful candidate will apply methods from economics, supply chain risk analysis, and data
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. The primary focus of this role will be in developing laboratory methods to improve recovery of microbial genomes from complex samples. Secondary focus of this role will be in maintaining cultures
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may include work at Jefferson Lab, the Electron-Ion Collider (EIC) program, detector research and development, and applications of AI in nuclear physics. Applications received by Tuesday, November 4
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well as university partners on coastal methods and validation strategies. Mentor summer students on data analysis and visualization workflows. Publish in peer-reviewed journals, present at scientific conferences, and
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following component failures Experimentally validating the AI/ML methods on the ATLAS linac at Argonne National Laboratory Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years
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, lithium-ion batteries, etc.), evaluating the competitiveness of mining projects, and developing economic models that support DOE priorities. The successful candidate will apply methods from economics
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The Applied Materials Division at Argonne National Laboratory has an immediate opening for a Postdoctoral Appointee. The candidate will be responsible for reviewing and developing design methods and
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considerable amount of experimental laboratory work with a variety of electrochemical based methods (galvanostatic/potentiostatic, AC impedance, and hybrid potential/current control methods) coupled