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, focused ion beam specimen preparation, and computer vision or machine-learning analysis of microscopy datasets. The position requires strong experimental, analytical, written, oral, and interpersonal
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The Nuclear Technologies and National Security Directorate (NTNS) is seeking a dynamic and passionate Postdoctoral Appointee with strong background in statistics or machine learning to lead an
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models in Argonne’s GREET model and related analytical tools. The appointee will evaluate current and emerging production pathways, including lithium production from brines and hard-rock resources, lithium
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frameworks such as PyTorch, TensorFlow, JAX, or similar tools. Knowledge of federated learning, distributed machine learning, privacy-preserving AI, foundation models, multimodal learning, continual learning
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skills, including experience with tools such as NumPy, pandas, scikit-learn, and machine learning frameworks such as PyTorch, TensorFlow, or similar Experience developing surrogate models, predictive
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. The successful candidate will contribute to Argonne’s industrial capacity planning, logistics optimization, and supply chain analysis models and apply these tools to support high-impact research on resilient
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that influence the development, intensification, and persistence of extreme events, using observational datasets, machine learning, and Earth system modeling. The successful candidate will work with observational
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. Proficiency in simulation tools used in analyzing vehicle energy consumption. Passion for and experience in data-driven modeling and analysis. Demonstrated ability to perform vehicle modelling and simulation as
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characterization, and machine learning. The role offers the opportunity to leverage Argonne’s world-class scientific capabilities and engage with a strong network of internal and industry collaborators. Position
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Manufacturing group perform science-based membrane synthesis and scaleup development by using roll-to-roll manufacturing and machine learning enabled in-line characterization and quality control methods