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. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be
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energy goals. ESIA also develops, deploy, and advance grid technologies that ensure a robust and secure U.S. grid transmission and distribution system. We collaborate with government agencies as
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, including both the large-scale production machines and the testbed machines featuring novel architectures such as Cerebras and SambaNova. The list below provides examples of the potential tasks
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operations is preferred, working knowledge of machine learning and artificial intelligence methods is highly desirable The successful candidate will demonstrate expertise in accelerator physics, accelerator
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-principles and atomistic simulations with machine-learned interatomic potentials to: Model reaction pathways on metal-oxide surface, including adsorption, reactions and diffusion steps. Construct atomistic
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values of impact, safety, respect, integrity, and teamwork Preferred Qualifications Deep understanding of AI/ML concepts, including transformers, latent-space representations, generative models, and
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computer-aided design software. Collaborative skills, including the ability to work well with other divisions, laboratories, and universities. Ability to demonstrate strong written and oral
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science, including electronic structure methods molecular dynamics, and scientific machine learning. Experience with High-Performance Computing (HPC) systems and intelligent workflows. Demonstrated
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
, or Julia) Experience in statistical modeling and probabilistic analysis Ability to model Argonne’s core values of impact, safety, respect, impact and teamwork Preferred skills, abilities, and knowledge
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skills and familiarity with LLM APIs (e.g., OpenAI API), agent frameworks (e.g. LangChain), PyTorch, and the Python scientific stack (e.g., numpy, pandas, scikit-learn). Experience with front-end