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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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utilize new machine learning methods for 3D behavior tracking and analysis. · Advise PhD students on related projects. Other Work Performed and Expectations · Document progress consistently and
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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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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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, ChIP-seq, and ATAC-seq, CRISPR and RNAi perturbation screens 3. Ability to build predictive statistical and machine learning models that integrate multiple data types, including linear and nonlinear ML
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environment at Duke is ideal for our translational research efforts. Applicants must : 1. Hold a PhD with relevant skillsets in programming (including Python) and machine learning methods for image
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interested in applicants that have experience in one or more of the following areas: satellite remote sensing, energy balance modeling, and machine learning. In addition to scientific expertise, the successful