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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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Policy Research Assistant Hybrid (Washington, DC or Durham, NC) Margolis Institute for Health Policy
health care delivery and payment reform. This position will support Duke-Margolis projects with a variety of complex activities in research, writing, and analysis of quantitative and/or qualitative data
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Policy Research Assistant Hybrid (Washington, DC or Durham, NC) Margolis Institute for Health Policy
or NC, respectively) and in-person team collaboration. Work Performed Support and perform a variety of complex activities in research, writing, and analysis of quantitative and/or qualitative data within
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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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inflammatory diseases including diabetes, cardiovascular, and Alzheimer’s disease. Successful candidates will lead projects using multi-omics datasets from large sample sizes of disease relevant tissues/cells
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image analyses of anatomical data in a supportive and collaborative environment. Our goal is to advance bioelectronic medicines: electrical stimulation, block, and recording of peripheral autonomic nerves
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degree in biological or medical sciences or another field using quantitative methods. Experience deriving meaningful hypotheses or insights from large datasets, as evidenced by first-authored publications
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). The candidate will contribute to the design, development, and implementation of strategies to convert noisy tomographic data into high-resolution structures of challenging biomedical targets. Emphasis will be