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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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will be expected to work both independently and as part of a team and positively contribute to a highly collaborative environment. Specific projects pertaining to this position include defining and
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, software engineering, and the life sciences. The group has access to Duke’s TEM facility and dedicated computational resources that are supported and maintained by Duke University's Research Computing unit
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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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polyfluoroalkyl substances (PFAS) in North Carolina, the International Network For Researching, Advancing, and Assessing Materials for Environmental Sustainability (INFRAMES), and more. Our location in
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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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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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in the popular SimNIBS software package. This project is a collaboration between Dr. Warren Grill and Dr. Angel Peterchev at Duke University and Dr. Axel Thielscher at the Danish Research Center
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Mentors for suggested guidelines for the Postdoctoral Appointee-mentor relationship Prompt disclosure to the mentor regarding the possession and desire to distribute materials, reagents, software
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Appointee-mentor relationship 5. Prompt disclosure to the mentor regarding the possession and desire to distribute materials, reagents, software, copyrightable and potentially patentable discoveries derived