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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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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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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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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
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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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The Applied Materials Division (AMD) in the Emergent Materials and Process Group at Argonne National Laboratory in looking for a Post-doctoral Appointee -- Pyrometallurgy. The candidate will perform
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of Pittsburgh, University of Texas Medical Branch and BARDA, aimed at advancing pandemic bio-preparedness through AI-driven forecasting. With advances in machine learning frameworks and emerging accelerator
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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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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
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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