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Field
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-population analysis, and integration of genomic data with longitudinal electronic health records. The scholar will have opportunities to lead projects using data from the Global Biobank Meta-Analysis
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spatial transcriptomic data. A demonstrated interest in data visualization and large-scale data analysis is highly desirable. The ideal candidate will have a keen interest in understanding complex
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useful genetic information can be measured while limiting direct exposure of the underlying genomic sequence. The successful candidate will help design, build, validate, and iteratively improve
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(particularly computer vision), (2) machine learning for data analysis, (3) sensor technologies (e.g., electromagnetic sensors), (4) design and integration of mechanical/electrical devices; and an interest in
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, geometric data analysis, and infinite-dimensional geometric structures. The two postdoctoral researchers will work closely together, together with professors Rafael Bailo and Mireille Boutin, to develop
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Develop computational pipelines and machine learning methods for genomic, clinical, or imaging data analysis. Analyze large-scale biobank, EHR, and/or imaging datasets. Apply statistical, deep learning, and
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of Biomedical Informatics; designs and develops novel computational tools for biomedical data analysis; performs large-scale analysis using omics data; assists in identifying new biomedical data analysis
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] dataset), along with open-source applications that support data processing and analysis grounded in Responsible AI principles. A core part of the NTO’s mission is to develop open data and computational
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to join an interdisciplinary project at the interface of single-cell genomics, data integration, and developmental biology. The project focuses on the integration and analysis of multi-modal single-cell
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an experienced Postdoctoral Researcher in Crop Genetic Resources and Informatics to apply genome informatics and quantitative genetic approaches to trait analysis in maize. Research will include work on maize