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electrolytes, particularly sulfide-based electrolytes, for energy storage applications Knowledge of interfacial engineering strategies, such as surface coatings, to suppress lithium dendrite formation Hands
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(typically completed within the last 0-5 years) in material science or related chemistry science with 0 to 1 year of post-graduate experience. Knowledge in the areas of materials science, metallurgical and
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or equivalent. Skill in devising and performing experiments to acquire data, using and maintaining research equipment and instruments, compiling, evaluating and reporting test results. Knowledge and experience in
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geophysical sciences, computer science, or machine learning with 0 to 2 years of experience Knowledge of deep learning, PyTorch/JAX, and scaling deep learning models to large GPU-based machines Technical
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, integrity, and teamwork Preferred Knowledge, Skills, and Experience Experience with superconducting nanowire single-photon detectors (SNSPDs), transition-edge sensors (TESs), or kinetic inductance detectors
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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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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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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