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model classifiers (PLS-DA, random forest, neural network, etc) towards unraveling materials structure-function relationships, and are familiar with optimization approaches such as genetic search, Bayesian
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to study chemical transformations in materials. 2. Artificial Intelligence Applications: - Leveraging conventional machine learning techniques for materials property prediction and Bayesian approaches
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key element of the two-beam acceleration concept Emphasize Bayesian optimization approaches and integrate these methods into the facility control system Design, execute, and analyze accelerator
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Postdoctoral Appointee - Superconducting Devices for Multipixel Single-Photon Detection and Multiplexed Readout Argonne National Laboratory invites applications for a postdoctoral research position
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evaluate advanced algorithms for applications such as secure and adaptive control, anomaly and attack detection, resilient decision-making, and AI-enabled operational support for highly distributed grids
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teams, academia and industry, as well as other national labs and agencies, to solve some of the world’s largest and most complex problems in science and engineering. Objective: The goal
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, and evaluation in distributed and privacy-aware settings. While the position is supported by an AI for Science project on privacy-preserving federated learning, the broader objective is to advance
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Coherent Diffraction Imaging (BCDI), ptychography, and X-ray Photon Correlation Spectroscopy (XPCS). The goal is to move beyond simple correlations to discover the causal, governing rules of defect-property
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application of ultrafast THz-pump and optical-probe techniques to detect narrow-band THz radiation and explore mode-selective dynamics in quantum and molecular systems. This work leverages state-of-the-art
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diverse data sets, applying advanced analytics, and leveraging ML/AI techniques to detect, quantify, and forecast global risks affecting sourcing strategies. It will also include assessing AI-driven demand