67 assistant-professor-computer-science-data Postdoctoral positions at Duke University
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Duke University, Office of the Dean, Pratt School of Engineering Position ID: Duke -FacultyResearchMento -POSTDOCSALAHSHOOR [#30318] Position Title: Position Type: Postdoctoral Position Location
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, United States of America [map ] Subject Areas: Machine Learning Computer Science Mathematics / applied mathmetics , Mathematical Sciences , Partial Differential Equations , Statistics Appl Deadline: none (posted 2025/08
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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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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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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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, 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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career. 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
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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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/bioinformatics, and data science. Work Performed · Work in highly collaborative inter-disciplinary environment with clinicians, econometricians, statisticians, and data scientists · Lead statistical analysis
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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