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the Project HOPE 1000 pregnancy cohort, a large, longitudinal study based in Durham, North Carolina, to investigate how prenatal and early-life environmental exposures influence immune-related outcomes during
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experience in life cycle assessment (LCA) and related tools for managing large data sets to evaluate natural resources needed to advance emerging technologies. The candidate will lead their primary project and
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mechanisms underlying risk for addictive disorders—with a primary focus on tobacco dependence and relapse—and translating this information to develop more efficacious interventions. This position is supported
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. Postdoctoral Associate Position Haynes Lab Be You. The Duke Human Vaccine Institute (DHVI) provides national and international leadership in the fight against major infectious diseases. We are a large
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will develop novel statistical and machine learning methods for any of the following: multi-omics data (such as bulk and large-scale single-cell RNA sequencing data, spatial transcriptomics, bulk and
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are diverse; with exciting ongoing grant-funded and pre- funded projects that include big-data, population health studies, mental health aspects of head and neck cancer outcomes, human papillomavirus (HPV), HPV
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, including development of new computational tools for processing large-scale biospecimen data Creation of novel machine learning frameworks for automated scientific analysis and discovery Design and
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groups that are historically underrepresented in physics departments. These researchers will work with Prof. Michael Troxel to lead large-scale structure (weak lensing and galaxy clustering) projects
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implementation of strategies to convert noisy tomographic data into high-resolution structures of challenging biomedical targets. Emphasis will be made in the development of scalable strategies required to analyze
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including but not limited to microbial ecology, biochemistry, genomics, biostatistics, molecular biology, microbiology, evolutionary biology. Familiarity with metagenomics data analysis, microbial