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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 fundamental mechanisms shaping plant communities and their response to climate change. Qualifications: • PhD in Ecology or a related field • Strong quantitative background (e.g. ecological theory
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data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental
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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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multiple affiliations. Be You. Duke School of Medicine’s interdisciplinary Duke Molecular Physiology Institute (DMPI), is seeking a highly motivated Postdoctoral Fellow with a PhD in computational biology
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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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support in organizing mathematical biology seminars and activities at Duke Math. The postdoctoral associate will teach one course per semester in the Department of Mathematics. Qualifications
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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