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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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data-intensive operations in scientific and AI applications. Investigate machine learning techniques to inform heuristic methods for routing optimization, bridging theoretical insights with practical
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-CCE Scaling Machine Learning. The HEP Division performs cutting-edge research facilitated through advanced detector development, high-performance supercomputing (HPC), and innovative electronic and
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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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including transient analysis, engineered system evaluation, and machine learning applications in modeling thermal fluid behavior of interest to reactor analysis. The candidate is expected to produce papers
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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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position to develop and apply advanced analysis methods, including artificial intelligence and machine learning algorithms and approaches, for x-ray science and instruments. These methods will accelerate
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A postdoctoral position on exascale atomistic simulations, AI/machine learning and data analysis of ferroelectric devices is available immediately at the Center for Nanoscale Materials (CNM
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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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relevant field at the PhD level with zero to five years of employment experience. Experience with deep learning frameworks (PyTorch, TensorFlow, JAX). Strong background in computational image processing and