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degree and PhD degree in meteorology, oceanography, physics or mathematics, with a strong interest in the application of statistical and data analysis methods excellent knowledge in UNIX/Linux and Unix
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) mathematics, physics, or a related STEM fields. Job Description: The Division of Biomedical Informatics and Data Science (BIDS) within the Department of Health Outcomes and Biomedical Informatics (HOBI
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biological, and live cell fluorescence imaging experiments. Associated structural analysis of the proteins by cryo-electron microscopy will be undertaken via collaboration with other workers. This full-time
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The Applied Mathematics and Plasma Physics (T-5) group within T-Division conducts research across a wide variety of topics including fusion science, space plasmas, numerical analysis, computational imaging
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, and microbial ID analysis. -In this context, you might be expected to generate new research ideas and lead development of new research proposals. -You will also be expected to design and develop a
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acquisition, higher-level interpretation of computational results, and comprehensive data analysis. These activities may be carried out in both field and laboratory settings. The UNCW postdoctoral scholar will
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on an optical table and integrating novel optics to microscopy are a requirement for this position. Knowledge in computational imaging, mathematical modeling, strong computer programming skills in MATLAB and/or
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modeling. The ideal candidate will be responsible for developing and applying probabilistic models to advance time-series analysis. Key areas of focus for this position include: 1)Probability Theory and
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postdoctoral position in methods development and analysis of multi-omics and clinical data for inflammatory skin diseases. The start date is October 1st, 2025, or as soon as possible thereafter. Research context
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postdoctoral position in methods development and analysis of multi-omics and clinical data for inflammatory skin diseases. The start date is October 1st, 2025, or as soon as possible thereafter. Research context