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The Earth and Environmental Sciences Area at Lawrence Berkeley National Laboratory (LBNL) seeks a postdoctoral researcher to develop and curate unique and cutting-edge AI-ready data for the U.S
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on reproducibility and open-source best practices. Demonstrated experience in geospatial data analysis and the management of large, gridded meteorological or environmental datasets (e.g., NetCDF
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to improve cryogenic calorimeter performance and optimize data analysis. Perform data analysis, support operations, and maintain electronics for the CUORE and CUPID R&D experiments. Support the development
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and data analysis), synchrotron-based techniques, machine-learning and non-learning based approaches to data analysis, and familiarity with unconventional superconductivity. Responsibilities: • Perform
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Python, R or a similar scientific programming language, with a focus on reproducibility and open-source best practices Demonstrated experience in geospatial data analysis and the management of large
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-quality requirements, and analysis-ready data structures. Develop machine learning models that integrate multimodal experimental and simulation data. Contribute to the development of Crucible Data Platform
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. Position description The Environmental Markets Lab (emLab – https://emlab.ucsb.edu ) and Clean Energy Transformation Lab (CETlab – https://cetlab.es.ucsb.edu/ ) at the University of California Santa Barbara
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, implement, and test novel MRI sequences, reconstruction methods, or post-processing techniques for research and clinical translation Develop and validate disease progression models and other data-driven
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or particle Physics, or a related field, A strong background in either nuclear structure, electron-scattering theory/experiment, or global data analysis, Demonstrated expertise in scientific computing and code
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data-generation and training workflows, including sampling, active learning, transfer learning, validation, and uncertainty or robustness analysis, to reduce the cost of generating excited-state training