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://sites.duke.edu/yanglab/). Be part of a dynamic, interdisciplinary team working to unravel the unknown functions of membrane ion channels and lipid transporters in human health. Develop novel therapeutic approaches
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on performance and funding. If interested, please contact Erika Juhlin (erika.juhlin@duke.edu). Provide scientific input for research activities such as proposal and manuscript development, project management
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• Writing fellowship applications as appropriate for the development of your career Choose Duke. This will be a minimum one-year appointment, contingent upon satisfactory annual reviews. Salary is
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, Brown University, and Eli Lilly & Company. Under the guidance of the PIs, co-investigators, and collaborators, the Postdoc Associate will be responsible for developing new statistical methods to empower
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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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the Interpretable Machine Learning Lab (https://users.cs.duke.edu/~cynthia/home.html ) for a scientific developer to work in collaboration with other researchers on machine learning tools that help humans make better
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to other scientific projects within the lab as well as your own. Write fellowship and/or grant applications as appropriate for the development of your career Present research findings at local and national
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, creative, individual to pursue applications in ultrasound microvascular imaging in an innovative, collaborative environment. We are engaged in both development of super-resolution ultrasound imaging and its
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-funded R01 project to develop and disseminate computational neuron models and their integration with TES and TMS electric field simulations in the popular SimNIBS software package. This project is a
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. Occupational Summary The David Lab at Duke University (www.ladlab.org ) is recruiting a postdoctoral fellow to join an established research group developing and applying DNA sequencing and computational