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multimodal federated learning across heterogeneous data types such as clinical, imaging, omics, text, and experimental data. The work will include developing approaches for continual model improvement
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, particularly at subseasonal and seasonal lead times. The successful candidate will lead simulations of convective systems using high-resolution regional and global models, evaluate model performance, and
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data and 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
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machine learning (ML) to address future physics and detector challenges. Current physics interests include Standard Model measurements and searches for new phenomena. We welcome applicants who are
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research strongly preferred Experience in deep learning including data collection, architecture development, model training, and validation Interest in software development, with particular emphasis on
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workflows into model development and scientific analysis. This position offers the opportunity to collaborate closely with experimental teams working in areas such as electrochemical characterization and
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for data fusion, physics-informed model validation, and causal discovery of defect–property relationships. Publish high-impact research results and present findings at national and international
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-based models for large dynamical systems (e.g. weather, hydrology). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with
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surface coatings, to suppress lithium dendrite formation Hands-on experience in the fabrication and testing of all-solid-state cells, including coin-cell and/or model cell configurations Knowledge of
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characterization; translate model predictions into experimental campaigns; and update models using newly acquired data Contribute to strategies for generating diverse, high-value datasets, identifying