40 parallel-processing-bioinformatics Postdoctoral positions at Duke University in postdoctoral
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Conduct independent and collaborative research investigating the molecular and cellular mechanisms of vision. Study the biological processes responsible for building and maintaining the specialized light
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Postdoctoral Associate to be part of an integrated experimental and computational team working to create focused antibiotic prodrugs that treat bacterial infections while sparing the gut microbiome. Building
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CT imaging CT reconstruction and image-processing methods Material decomposition and quantitative spectral imaging Image segmentation, registration, and quantitative morphometry Machine learning and
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. Postdoctoral Associate – Dept. of Surgery (Lyerly Lab) Be You. The Department of Surgery is seeking a detail-oriented Postdoctoral Associate to be part of the Lyerly Lab. This position will generate, validate
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experiments. Utilize laboratory techniques that support hypothesis-driven neuroscience research. Analyze and Interpret Data Process, interpret, and visualize scientific data using tools such as R, Python
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evaluation of machine learning, computer vision, and other algorithms, primarily in the context of health. They will be part of the thriving research community of Duke Spark (spark.duke.edu) where AI
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. The Aleks Tata lab in the Department of Surgery, Duke University School of Medicine has an opening for postdoctoral researchers to study mechanisms in lung injury-repair. We seek to understand
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aspects of the research process. Current work in the Lab aims to address three questions: (1) What are the core components of beliefs about the prevalence and nature of suffering? (2) How do these beliefs
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. Postdoctoral Associate – Dept. of Surgery (Haines) Be You. The Department of Surgery is seeking a Postdoctoral Associate with strong clinical research, project management, and data science skills to support a
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) Evaluate the implications of these associations for extraction processes; and 3) Assess the critical mineral resource potential of unconventional wastes through geospatial and statistical analysis of supply