49 computational-physics-superconductor Postdoctoral research jobs at Duke University
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quantitative methods and excited about discovering physical principles of biological organization. Minimum Requirements: PhD in a scientific disciplines, ideally Biology, Bioengineering, Physics or Math
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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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, and behavioral measures. · Collect, process, and analyze multimodal datasets, including neural, physiological, and motion-tracking signals. · Develop and refine research protocols and methodologies in
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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, Electrical and Computer Engineering Position ID: Duke -Electrical and Computer Engineering -POSTDOCYIRANCHEN [#30336] Position Title: Position Type: Postdoctoral Position Location
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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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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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structures. 3) Characterize the dynamics, mechanism, and pathway of self-assembly process and the structural and mechanical properties of the obtained assemblies 4) Compare the computed properties against
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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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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control