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Berkeley Lab’s Energy Geosciences division is hiring an Autonomous Material Processing Postdoctoral Fellow to help transform how critical materials are produced. In this role, you’ll bring together
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, and advanced sequencing technologies to generate large-scale datasets. In parallel, you will develop and apply computational pipelines that integrate sequence analysis, structural modeling, and
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to improve genome annotation. This role centers on applying and extending computational approaches to study genomes and multi-omics datasets, with an emphasis on understanding biological processes relevant
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composite dielectrics for advanced energy storage applications. This position is part of a multidisciplinary program focused on the design, synthesis, processing, and characterization of organic–inorganic
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transformations, and the interfacial phenomena occur between the electrode and electrolyte. Perform advanced diagnostic studies to probe key phenomena and reactions occurring during battery operation, particularly
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managing daily laboratory resources, processing and shipping biological specimens with dry ice, obtaining liquid nitrogen, preparing lab kits and supplies, and disposing of biohazard materials. The role also
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processing, memory and cognitive control using techniques including fMRI, EEG, and psychophysiological recordings (EMG, EDA). Ongoing projects include studies of the prefrontal organization of cognitive
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or airborne remote sensing data; processing ecosystem flux data. Familiarity with carbon-water coupling, vegetation physiology, drought impacts, land-atmosphere interactions, or plant pathology. Interest in
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experience with managing, processing, and analyzing large datasets and strong programming skills especially in Python. ● Experience working with transmission power flow and machine learning models. ● Strong