71 assistant-professor-computer-science-data Postdoctoral positions at Duke University
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, United States of America [map ] Subject Areas: Computer Science Mathematics / applied mathmetics , Mathematical Sciences , Partial Differential Equations , Statistics Machine Learning Appl Deadline: none (posted 2025/08
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decoding in real-world and virtual reality environments. Key Responsibilities: · Design and implement experiments studying memory and navigation, integrating intracranial recordings, wearable sensor data
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alone ––without a deep understanding of Ecology or Evolutionary Biology would in principle not be enough for this position. Fluency in data analysis in R, and strong experimental skills are essential
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mentoring, if needed. Minimum Qualifications The candidate should have a Ph.D. in Engineering, Applied Mathematics, Computer Science, or a related area. Experience 0+ years of postgraduate experience. Skills
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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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. The Department of Biochemistry is looking for a postdoctoral associate to work in the lab of Dr. Kate Meyer on research projects. Minimum Requirements: PhD in Molecular Biology, Genetics, Biology
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The research will be studying skeletal muscle biology and function in regulating other tissues, using cellular, molecular, and model animal (transgenic mice) approaches. Specifically, the postdoctoral research
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will be studying skeletal muscle biology and function in regulating other tissues, using cellular, molecular, and model animal (transgenic mice) approaches. Specifically, the postdoctoral research
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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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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control