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description CENSEMAT investigates crystalline energy materials in which non-random local deviations from the average structure play a decisive role. The postdoc will develop atomistic models that connect local
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mechanical testing to uncover processing–microstructure–property relationships. The candidate will have opportunities to interact with multidisciplinary teams and contribute to high-impact publications and
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participants make effort-based decisions and perform calibrated actions while physiological and movement data are continuously recorded. Participants will complete immersive behavioral tasks designed to measure
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funding. Communicate research findings through technical reports, conference presentations, and timely publications in peer-reviewed journals. Prepare invention disclosures and contribute to patent
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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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have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be up to 24 months
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, including feature engineering, management of missing data, model selection, calibration, discrimination, decision-curve analysis and assessment of clinical utility, alongside internal, external and temporal
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datasets, including scRNA-seq, scATAC-seq, scCUT&Tag, HiC, and proteomics, with an emphasis on trajectory inference, regulatory dynamics, and cell fate decisions. A key goal is to integrate multimodal data
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recruit and train the next-generation of data-driven life scientists and to create globally leading computational and data science capabilities in life science in Sweden. Research project The rapid
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decision making in SCD, particularly as it related to chronic SCD pain. The lab has received NIH funding to complete a longitudinal study of chronic pain phenotypes in SCD (SPIRIT2). Education: The post