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analysis and machine learning methods applied to protein structure determination using single-particle cryo-electron tomography (ET). The candidate will contribute to the design, development, 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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. The Postdoctoral Associate will apply his/her technical skills toward development and implementation of machine learning, computer vision, and other algorithms for analysis of medical images and prognostication as
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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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learning is desired. The application needs to include: CV and statement of research interests and three names of references. Consideration of applications will begin immediately, and will continue until
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27710, United States of America [map ] Subject Areas: Data Science / Machine Learning Biostatistics / Biostatistics and Data Science Statistics / Statistics Appl Deadline: none (posted 2025/02/12
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, including development of new computational tools for processing large-scale biospecimen data Creation of novel machine learning frameworks for automated scientific analysis and discovery Design and
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seek a full-time postdoctoral researcher to work at Duke University on a project at the interface of neuroscience and machine learning. We seek to advance our understanding of neural systems and
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) and bioinformatics tools Familiarity with data science, machine learning, artificial intelligence, natural language processing and applications to electronic health records and big data and
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groups, and/or institutional analysis. Applicants should demonstrate interdisciplinary literacy and be familiar with or willing to learn approaches to integrate Indigenous Environmental Governance