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Field
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Postdoctoral Research Associate will contribute to developing and/or applying artificial intelligence, machine learning, and image segmentation and/or data science-based methods to address cutting-edge topics
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 9 hours ago
looking for a postdoctoral fellow interested in developing either machine learning algorithms for high-resolution histopathology imaging/spatial-profiling data in combination with other modalities (e.g
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periods of time, repetitive motion related to computer work, physical activity related to lab and fieldwork; including regular standing, walking, or sitting; using hands to handle or feel; reach with hands
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nano-mechanics, and machine learning as it applies to the field of computational mechanics. Candidates will be given opportunities to develop their teaching experience by designing and teaching a class
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survival data using longitudinal features, and (6) machine learning and deep learning for analyzing time-to-event outcomes, or (7) radiomics and medical imaging analysis. Required Qualifications: We seek
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from new and existing datasets. Contribute expertise (e.g., disciplinary, educational, methodological) to ongoing research projects in the PrepareCS project. Manage and support professional learning
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The Opportunity: The Post Doctoral Scholar will be part of a collaborative initiative between the Department of Electrical and Computer Engineering at the University of Central Florida (UCF) and
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analysis; Biomarker identification through the use of machine learning approaches; and Multi-omics data integration with genomics, transcriptomics and methylomics data. Job Description Primary Duties
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computer science using data-driven techniques (graph theory, ICA, machine learning), in other imaging modalities (DTI; MEG), and in multimodal integration will be relevant. Experience with AFNI/SUMA, SPM, FSL
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Postdoctoral Researcher position in AI and machine learning, with a focus on patient-specific reconstruction of coronary vessels and the simulation of stenting techniques using Finite Element Analysis (FEA) and