66 assistant-professor-computer-science-and-data-"Meta" Postdoctoral positions at Duke University
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innovative statistical methods in clinical trial design and variable selection methods in high dimensional data that will predict clinical outcomes and meta-analyses. The successful candidate will collaborate
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Duke University, Electrical and Computer Engineering Position ID: Duke -Electrical and Computer Engineering -POSTDOCYIRANCHEN [#30336] Position Title: Position Type: Postdoctoral Position Location
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, United States of America [map ] Subject Areas: Chemistry / Bioinformatics , Chemical biology , Computational Appl Deadline: 2025/09/15 11:59PM ** Position Description: Apply Position Description Job Opening: Postdoctoral
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application to medical imaging (e.g., MRI) · Experience with MRI data analysis, network science, graph theory, topological analysis, or related computational approaches, especially in Alzheimer’s
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Duke University, Office of the Dean, Pratt School of Engineering Position ID: Duke -FacultyResearchMento -POSTDOCSALAHSHOOR [#30318] Position Title: Position Type: Postdoctoral Position Location
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single-cell multi-omics data, or genetic variants and functional genomics data), and apply the computational methods to integrative genomics data to understand tumor microenvironment, brain science, and
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, including Computer Science, Electrical and Computer Engineering and Music and will be expected to participate in the preparation of research proposals and scientific publications. The candidate will also be
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/bioinformatics, and data science. Work Performed · Work in highly collaborative inter-disciplinary environment with clinicians, econometricians, statisticians, and data scientists · Lead statistical analysis
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, Duke University Biology Department to study how archaeal microbial communities respond to stress in hypersaline environments. A PhD in computational and/or experimental biology is required in fields
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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control