40 distributed-computing-"UNIS" Postdoctoral research jobs at Duke University in United States
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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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Duke University, Computer Science Position ID: Duke -CS -PDA_RUDIN25 [#30110] Position Title: Position Location: Durham, North Carolina 27708, United States of America [map ] Subject Area: Computer
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. The appointment is not part of a clinical training program, unless research training under the supervision of a senior mentor is the primary purpose of the appointment. The Postdoctoral Appointee functions under
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Duke University, Nicholas School of the Environment - Durham Program ID: Duke -NSOE-Durham -POSTDOC_MEYER [#28186] Program Title: Postdoctoral Associate - Integrated Toxicology & Environmental
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Gastroenterology Research Training Program Postdoctoral Fellowship The Duke Division of Gastroenterology is seeking applicants for a two-year post-doctoral fellowship within the Duke Gastroenterology Research
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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
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develop novel computational approaches. Develop mathematical descriptions for the acquired data and work with our theorists collaborators to implement new theories. Integrate with the rest of the lab and
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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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Assessment Models (IAMs) such as GCAM or PAGE. The candidate must have a PhD degree in a related field, be fluent in computer programming, preferably python, and will ideally have experience in working with
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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