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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 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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, 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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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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Duke University, Biostatistics & Bioinformatics Position ID: Duke -Biostatistics & Bioinformatics -PD245471 [#29120, 245471 - Halabi] Position Title: Position Type: Postdoctoral Position Location
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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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theory, multi-objective optimization and machine learning. The specific project aims to understand the multiscale interactions shaping human gut bacteria and human gut pathogens. The project will combine
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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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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