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, United States of America [map ] Appl Deadline: (posted 2025/09/04, listed until 2026/02/20) Position Description: Apply Position Description Postdoctoral Associate – Scientific Machine Learning for Multiscale Biological
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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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Duke University, NSOE-ENS & ECS Position ID: Duke -NSOE-ENS & ECS -POSTDOC_HOFFMAN [#30281] Position Title: Position Type: Postdoctoral Position Location: Durham, North Carolina 27708, United States of America [map ] Subject Areas: Environmental Health Biostatistics Epidemiology Appl...
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of Civil and Environmental Engineering / Chaney Lab: Perform the core of the proposed research activities including processing the remotely sensed LST to compute the spatial statistics, run the HydroBlocks
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appointment is generall y preparatory for a full time academic orresearch career.The appointment is not part of a clinical training program, unlessresearch training und er the supervision of a senior mentor is
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. Postdoctoral Associate - Cognitive Neuroscience of Legal Decision MakingAn interdisciplinary team of lawyers and cognitive neuroscientis ts atDuke University seeks apostdoctoral associate tojoin a recently fun
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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 research
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. The Eroglu lab in the Duke University Department of Cell Biology seeks a full-time postdoctoral associate. For information about the lab and the PI, please visit: https://www.cellbio.duke.edu/cagla
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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 the supervision of a mentor
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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