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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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University School of Medicine along with the Duke University School of Nursing, and Duke University Health System comprise Duke Health, a world-class academic medical center. The Health System encompasses Duke
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the possession and desire to distribute materials, reagents, software, and copyrightable and potentially patentable discoveries derived from the Postdoctoral Appointee's research. Collegial conduct towards members
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University School of Medicine along with the Duke University School of Nursing, and Duke University Health System comprise Duke Health, a world-class academic medical center. The Health System encompasses Duke
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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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methods to compare the new designs and techniques with existing ones in binary, continuous, and time-to-event outcomes. The Postdoc Associate will help develop R/SAS software for the proposed statistical
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productivity, labor productivity, and GDP that are driven by either climate change or air quality changes. Work will include use of output from global climate models (CMIP-class models) as well as Integrated
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University School of Medicine along with the Duke University School of Nursing, and Duke University Health System comprise Duke Health, a world-class academic medical center. The Health System encompasses Duke
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the possession and desire to distribute materials, reagents, software, copyrightable and potentially patentable discoveries derived from the Postdoctoral Appointee's research. Collegial conduct towards members
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, neuroscience, physiology, physics, or computer science · Be a proficient programmer and experience in Python, MATLAB, NEURON, COMSOL, and / or git are assets · Be familiar with neural biophysics and