59 computer-vision-and-machine-learning Postdoctoral positions at Duke University in United States
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, United States of America [map ] Subject Areas: Mathematics / applied mathmetics , Mathematical Sciences , Partial Differential Equations , Statistics Machine Learning Computer Science Appl Deadline: none (posted 2025/08
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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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, and Alzheimer's research. Qualifications: · Ph.D. in Computer Science, Biostatistics, Bioinformatics, Biomedical Engineering, or a related field · Expertise in deep learning and its
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with various methods that can incorporate domain-based constraints and other types of domain knowledge into machine learning and applying these techniques to problems in computational creativity
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policies pertaining to other schools at Duke University. The postdoc candidate is expected to: 1) Develop novel methods for incorporating scientific machine learning in solving problems in solid mechanics
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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
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. The Department of Biochemistry provides a rich intellectual environment, with research in structural biology (cryo-EM, X-ray crystallography, NMR spectroscopy), and computational biology. Duke’s benefits package
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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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collaboration with Dr. Suthana and interdisciplinary team members. · Apply advanced statistical and computational approaches to investigate neural dynamics underlying memory consolidation and navigation