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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
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skills and qualifications: A recent PhD (completed within the last 5 years) in computer science, electrical engineering, or a related field. Strong background in network interconnect design and
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communication skills, and ability to interact with people at all levels both within and outside the laboratory. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork
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is typically achieved through a formal education in chemical engineering, chemistry, materials science, nuclear engineering, mechanical engineering, or related field at the PhD degree level with zero
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in high-voltage battery systems through a fundamental understanding of interfacial mechanisms. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in Organic Chemistry
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to develop, synthesize, characterize and electrochemically evaluate next generation cathode materials for lithium-ion and sodium-ion batteries. Position Requirements Recent or soon-to-be-completed PhD
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and datasets • Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term
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Energy Systems and Infrastructure Analysis Division. We are seeking applicants with a strong technical background and expertise in international trade modeling, particularly in the upstream automotive
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Leadership Computing Facility (ALCF), the Mathematics and Computer Science Division (MCS), the Computational Science Division (CPS), and the Data Science and Learning Division (DSL). The postdoctoral
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-be-completed PhD (typically completed within the last 0-5 years) in chemistry, chemical engineering, material science, or related fields. Demonstrated knowledge in materials synthesis and