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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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Doctoral Associate Duke Department of Neurosurgery This Postdoctoral Associate position will play a critical role in making the use of new imaging tools more automatic and streamlined. Key responsibilities
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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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& Behavioral Sciences at Duke University School of Medicine. Our research combines laboratory behavioral pharmacology, ecological momentary assessment, and functional neuroimaging to examine neurobehavioral
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, meta-analysis, and/or observational approaches to provide robust evaluation of generality of species-interaction effects across space, time and physical gradients. We work in salt marshes, seagrasses
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candidates will possess excellent oral and written communication skills with the ability to work in a highly collaborative team environment. Be Bold. Position Description: HLA-E-VL9 antibody in gnotobiotic
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to characterize the chemical speciation and leaching potential of target metals from candidate feedstocks. The Postdoctoral Associate should be able to work effectively with collaborators from diverse disciplinary
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, United States of America [map ] Subject Areas: Climate Science Atmospheric Sciences Quantitative Analysis Appl Deadline: (posted 2025/05/12, listed until 2025/06/23) Position Description: Apply Today is the last day you can
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analysis tools Experience in machining learning methods in omics analysis Experience with high-performance computing and cloud-based analysis platform Previous experience in grant writing and manuscript
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directions that can seed future independent positions. Work Performed Depending on candidate interests and expertise, projects may involve: Analysis of global dietary patterns using genomic approaches