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analysis Interdisciplinary Collaboration - Experience working in cross functional teams including molecular biologists, chemists, radiation experts and computational biologists Core Values - Ability to model
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High-Energy Physics (HEP). We seek highly qualified candidates with interest and experience in ML algorithms including unsupervised techniques, time-series modeling, and clustering algorithms
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. Ability to work with large volumes of hazardous chemicals. Flexibility to change projects and work on a variety of projects simultaneously. Ability to model Argonne’s core values of impact, safety, respect
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material property database for composites. The candidate will utilize the database to develop AI models for composite discovery. The candidate will work with a multidisciplinary team to set up finite element
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modeling is a benefit, ideal candidates will be expected to work together with domain experts rather than possess all required expertise themselves. Beyond the listed projects, the candidate will be able
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operando experiments under electrical, thermal, or mechanical bias to capture real-time defect dynamics. Integrate multimodal datasets and collaborate with AI/ML teams for data fusion, physics-informed model
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Flexibility to engage across multiple projects, research topics, and applications Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Ability to maintain a full-time, on
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) simulations and reduced order modeling of turbulent and reacting flows relevant to advanced propulsion and power generation systems, such as gas turbines and detonation engines. The successful candidate’s
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survey analysis Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Preferred Qualifications Background in observational cosmology, galaxy clusters, or wide-field
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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors