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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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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
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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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-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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models for turbulent reacting flows. Development and application of machine learning tools in one or more of these areas: chemical kinetics, turbulent combustion, and extreme event prediction. Job Family
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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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and methods, fostering innovation and accelerating progress in the development of efficient solar energy conversion technologies. Position Requirements Recent or soon-to-be-completed PhD (within
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requires not only expertise in LLMs and machine learning but also an understanding of the unique challenges posed by scientific data, which often includes large-scale numerical datasets, complex simulations
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis