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the Bartesaghi Lab at Duke University to work in the development of image analysis and machine learning methods applied to protein structure determination using single-particle cryo-electron tomography (ET
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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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, 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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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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) 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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medical research needs Solid background in causal inference and survival analysis Experience with clinical trial research, machine learning, and high-dimensional statistics (desirable but not required
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novel statistical methods motivated by medical research needs Solid background in causal inference and survival analysis Experience with clinical trial research, machine learning, and high-dimensional
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27710, United States of America [map ] Subject Areas: Statistics / Statistics Data Science / Machine Learning Biostatistics / Biostatistics and Data Science Appl Deadline: none (posted 2025/02/12
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-analysis project, Bayesian background with experience in hierarchical modelling and mixed effect models is preferred. The second project, knowledge in survival analysis and machine learning is desired
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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