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Argonne’s core values of impact, safety, respect, integrity, and teamwork. Preferred Knowledge, Skills, and Experience Experience applying machine learning or AI techniques to scattering, imaging
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devising and performing experiments to acquire data, using and maintaining research equipment and instruments, compiling, evaluating and reporting test results. Knowledge and experience in chemical
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, Illinois five days per week. Must be able to respond to issues in the laboratory quickly. Preferred Qualifications: Knowledge in the following areas and techniques: ion-selective sorbents and membranes, 2D
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Requirements Required skills, abilities, and knowledge: Recent or soon-to-be completed PhD (within the last 0-5 years) by the start of the appointment in computer science, electrical engineering, applied
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related discipline. Expertise in Nek5000/NekRS or other comparable spectral element method codes. Experience in running high-fidelity simulations on leadership class supercomputers. Knowledge of performing
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ellipsometry, atomic force microscopy, and scanning electron microscopy is required. Knowledge of atomic layer deposition and materials for energy storage applications is highly desirable. The successful
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. Willingness to learn basic domain-specific X-ray science and basic materials knowledge, and a strong passion for applying agentic AI to scientific discovery. Effective written and oral communications skills
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to comply with federal regulations and contract. This level of knowledge is typically achieved through a formal education in economics, operations research, public policy, environmental science, data science
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related field. Experience with finite element simulations and developing constitutive models. Knowledge of high temperature creep crack growth. Knowledge of engineering design codes such as the ASME Boiler
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. Integrate domain knowledge from power systems with modern ML methods to create physics-informed, interpretable, and operationally relevant solutions. Build and evaluate models using realistic utility or test