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ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experiment, and who is motivated to translate computational advances into real
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position will contribute to the Forge project, which is focused on developing advanced multimodal AI capabilities that can learn across distributed data environments without requiring sensitive data to
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; assisting with data analysis and technical documentation; and contributing to publications, reports, and sponsor deliverables. The successful candidate will work closely with staff researchers and
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thin films and heterointerfaces. The postdoc will lead experimental design, data acquisition, and quantitative reconstruction. The appointees will work within a highly collaborative team spanning
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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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. The successful candidate will develop a novel multiscale multimodal experimental apparatus with precise control during data acquisition, as well as work on data processing pipeline, adaptable to imaging
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electropolishing, flash polishing, and focused ion beam methods. Experience applying computer vision, image analysis, and/or machine-learning methods to microscopy or materials characterization data
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-material production, and recycling; compile life-cycle inventories from scientific literature, technical reports, patents, public industrial information, supplier data, and other sources; translate
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-duty vehicles, hybrid, battery electric and fuel cell powertrain design, development of supervisory control logic, validation of vehicle models against test data etc. The projects and interests of this
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closing this gap. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary