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multidisciplinary team comprised of fellow postdoctoral appointees, experimentalists, and staff scientists, with computational fluid dynamics (CFD) and artificial intelligence/machine learning (AI/ML) expertise, with
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experience: Familiarity with machine learning hardware Familiar with high performance scientific infrastructure Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term
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. Develop advanced optimization, control, or machine learning strategies for distribution systems; validate these strategies using hardware-in-the-loop or real-time grid simulators. Develop optimization
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with a team. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Preferred Knowledge, Skills, and Experience Experience in machine learning/deep learning methods
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-of-the-art data management, machine learning and statistics techniques. With the advancement of Exascale systems and the variety of novel AI hardware designed to accelerate both training and inference
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, inclusive, and accessible environment where all can thrive. Additional Preferred Qualifications: Working knowledge of power system protection and control. Familiarity with Machine Learning. Familiarity with
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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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across the EFRC center Present research findings in reports, peer-reviewed publications, and at scientific conferences Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years
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at Jefferson Lab Innovation in detector technologies such as 3D-printed pixelized MCP-PMTs and superconducting nanowire single-particle detectors Position Requirements Recent or soon-to-be-completed PhD (within
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data analysis/spectral image processing. Use of data analytics or machine learning to guide process design and optimization. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long