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and mathematical modeling, hierarchical statistical modeling, machine learning, remote sensing, geospatial statistics) • Demonstrated ability to conduct independent research and publish high-quality
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will develop novel machine learning and artificial intelligence (ML/AI) methods for genomics data, especially: large-scale single-cell genomics data, high-definition spatial genomics, digital pathology
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for genomics (e.g., generative models, transformers, agentic workflows) and/or statistical learning (e.g., network & spatiotemporal modeling, functional/longitudinal data, time-series). Analyze single-cell
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the global health scenario and domestically for dissemination, and plenty of opportunities for career advancement. •Learn background/research methods of studies for which analysis is conducted with limited
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scientists (graduate students and technicians) · Flexing one’s intellectual muscles to think out-of-the box for this difficult-to-cure cancer Required Qualifications at this Level · PhD (or doctoral degree
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brain evolution. We employ a multifaceted strategy to bridge developmental neurobiology, RNA biology, and evolution. Learn more about our interests, motivations and discoveries: https://sites.duke.edu
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, genomics, computer science, bioinformatics, or a related discipline. The successful candidate will lead computational research projects applying advanced statistical, machine learning, and artificial
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Description: (PhD required) · Conduct critical experiments and data analysis of federal-funded ongoing AMD research including, but not limited to, Immunohistochemistry/immunofluorescence on human and animal
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healthcare. Qualifications Required: PhD (or equivalent) in computer science, statistics, biostatistics, electrical/biomedical engineering, or related quantitative field. Strong background in machine learning
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collaboratively, and a compelling explanation of how humanistic methods will be used in the completion of the year-long project. Candidates are expected to hold a PhD in a humanistic field at the time of