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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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, generous retirement benefits, and a wide array of family-friendly and cultural programs to eligible team members. Learn more at https://hr.duke.edu/benefits/ Duke is an Equal Opportunity Employer committed
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-performance computing. Additional experience in DNA biophysics, machine learning, and optimization is preferred. This job description intends to provide a representative and level of the types of duties and
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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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system. For the meta-analysis project, Bayesian background with experience in hierarchical modelling and mixed effect models is preferred. The second project, knowledge in survival analysis and machine
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27710, United States of America [map ] Subject Areas: Data Science / Machine Learning Statistics / Statistics Biostatistics / Biostatistics and Data Science 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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) and bioinformatics tools Familiarity with data science, machine learning, artificial intelligence, natural language processing and applications to electronic health records and big data and