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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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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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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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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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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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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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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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, 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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computer vision models and web-based decision support tools, to enhance efficiency and decision-making in swine production. Required Minimum Qualifications: • Ph.D. in Animal Science, Swine Nutrition
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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