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proficient in performing chemical reactions under high-temperature and high-pressure conditions incorporating various approaches including catalytic processes, oxidizing/reducing environments, different gases
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and novel AI hardware to help solve significant real-world problems using machine learning and deep learning. ALCF researchers work in a highly collaborative environment involving science application
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scientist Familiarity working in a National Laboratory setting and accompanying experience with safety protocols and working environment Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type
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, reconstruction, and analysis workflows Experience working in collaborative, multidisciplinary research environments Applicants should submit Curriculum vitae Brief description of research interests Three letters
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environments. Analyze products for impurity content through techniques such as XRD and SEM-EDS to ensure that products meet sponsor requirements. Communicate effectively with supervisors, peers, and Laboratory
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techniques to solve pressing challenges in energy storage. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne
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environments (MPI, batch schedulers). Hands-on experience with geospatial data processing (NetCDF/HDF5, GDAL, xarray) and coastal datasets (e.g., GEBCO/ETOPO, USGS/NOAA/NCEI), including mesh generation and
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visualization systems using realistic scientific data and simulations, including applications in cosmology, materials, fusion, environmental science, and light source data Collaborate closely with domain
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ability to scale models using distributed computing environments. Excellent oral and written communication skills for effective collaboration across multiple teams. Commitment to embodying the core values
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deposition methods and equipment. Skills working interactively and productively in a multidisciplinary environment. Skills in oral and written communication. Record of publication and external recognition in