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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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. 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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, 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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in either an academic, government or corporate setting. They would also have a PhD or commensurate industry experience/expertise in a Cybersecurity related discipline, such as Computer Engineering
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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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user defined. Manage project to develop machine learning algorithms designed to mine data from the digital database, advancing the research capabilities of the center. Write grants and reports related
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develop new modeling methods and new models and apply them to study vascular disease. The applicant will gain exposure to other projects in the lab, spanning machine learning, wearables, and physics-based
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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
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language processing, data-mining, machine learning, spatial analysis Knowledge of workflows for digital media (streaming and non-streaming) Experience successfully supervising graduate or undergraduate students
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bioinformatics area) position is open at Duke University School of Medicine in the lab of Dr. Yi Zhang starting Jan 2024 or later. The ideal candidate will develop novel statistical and machine learning methods