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experience in life cycle assessment (LCA) and related tools for managing large data sets to evaluate natural resources needed to advance emerging technologies. The candidate will lead their primary project and
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) This postdoctoral fellow will focus on evolving and characterizing next-generation therapeutic proteins using the lab’s high-throughput phage-assisted continuous evolution (PACE) platform. Their work will contribute
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innovation, establishment of a seagrass farm, and monitoring of a large living shoreline project. In addition to research, the post-doctoral scholar will be required to teach a 4-5 week-long field course each
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. The Yang Lab at Duke University is seeking a motivated postdoctoral fellow to carry on exciting projects focusing on membrane transport biology. The postdoc will work in a dynamic, interdisciplinary
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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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and equipment. · Design, execute and document experiments with sufficient detail to understand and repeat by other lab members. · Update lab members on new developments in commercial reagents and
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, surveillance, control, survivorship, quality of life, health disparities and social determinants of health. Our projects are both US-based and international, and given our transdisciplinary focus, our projects
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work with graduate students and help them with their experiments. This position is funded by NIH. DEFINITION: The Postdoctoral Appointee holds a PhD or equivalent doctorate (e.g. ScD, MD, DVM
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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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- Executing experimental designs Data Analysis and Presentation/Writing/Publication (20%) - Recording and annotating datasets - Interpreting results to draw conclusions and design subsequent experiments