390 computer-"https:"-"APOS-UFFICIO-CONCORSI-DOCENTI" "https:" positions at Duke University
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related disciplines with experience in optics, computation and electronics. Responsibilities will include designing and implementing optical systems based on quantitative phase imaging with digital
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academic institutions. Individual will develop and test novel computational models of the neural activity generated by electrical stimulation of the brain. Also, perform data analysis utilizing medical
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The Associate in Research will be responsible for using and developing computational algorithms to analyze single-cell and spatial-omics datasets. Specifically, we have multiple projects where we are generating
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of Biostatistics and Bioinformatics is seeking a highly motivated and detail-oriented Post-Doctoral Associate to join our interdisciplinary team focused on developing computational models of the immune response
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other Duke faculty, or faculty at other academic institutions. Individual will develop and test novel computational models of the neural activity generated by electrical stimulation of the brain. Also
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Description: Apply Position Description Position Description: The Duke Master of Interdisciplinary Data Science (MIDS) program invites applications for an Adjunct Assistant Professor position with expertise in
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dynamics. Experience in synthesis, protein chemistry, spectroscopy, modern computational methods and excited-state dynamical studies is desirable. This is a short-term position, available for six months. A
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. Applicants should have a BS or higher degree in a biological or engineering discipline, or be currently enrolled in such a degree program. Must have experience with histological techniques and animal research
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Professor in the area of semiconductors, nanoelectronics, or computer engineering. This recruitment is part of the historic $57M gift from the Lamond family announced in January 2026 (see here: https
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nonlinear dynamical systems to join our interdisciplinary team. Our research focuses on developing and applying computational frameworks—including machine learning, nonlinear dynamical systems, and hybrid