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Postdoctoral Associate, Computational Behavioral Analysis & Digital Autism Research Be You. At Duke, we celebrate individuality, innovation, and the unique perspectives that each member of our
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Position Description The DukeNLP group in the Department of Computer Science at Duke University invites applications for a postdoctoral associate position in natural language processing and machine
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or scholarship, and may include teaching responsibilities. The appointment is generally preparatory for a full time academic or research career. The appointment is not part of a clinical training program, unless
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Date: Nov. 1, 2026 Application Review Begins: Immediately Location: Durham, North Carolina Work Arrangement: On-site Salary: $65,000 Research Program Description
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computational immunology as part of the NIH/NIAID-funded Multiscale Immune System Modeling (MISM) Center. The postdoctoral associate will contribute to and participate in meetings, workshops, trainings
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career. The appointment is not part of a clinical research training program, unless research training under the supervision of a senior mentor is a primary purpose of the appointment. The appointee works
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for postdoctoral fellows. We are seeking highly motivated candidates to join our research program in regeneration genomics. Our laboratory develops and applies cutting-edge genomics and proteomics technologies
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(Microbiology & Immunology, University of Michigan) Minimum Requirements Ph.D. in applied mathematics, computational biology, quantitative biology, or a related field. Experience in ABM development and ODE/PDE
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Position Description Duke University invites applications for a Postdoctoral Associate position in Statistical Science under the mentorship of Professor Eric Laber. This position will be involved with conducting research at the intersection of reinforcement learning and statistics. The primary...
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evaluation. Automate routine data management and quality assurance processes. Utilize version control and best practices for code management and documentation. Optimize computational efficiency across research