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Duke University, Economics Position ID: Duke-Econ-PD2025 [#30253] Position Title: Position Type: Postdoctoral Position Location: Durham, North Carolina 27708, United States of America [map ] Subject Area: Economics / 00 Default:Any Field Appl Deadline: 2025/07/24 11:59PM ** filled (posted...
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models that include these mechanisms. The postdoc will develop biologically-constrained machine learning–based model discovery pipelines to derive interpretable surrogate ODE/PDE models from simulated ABM
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discovery and computational tools. The successful applicant will lead a research project and will have the opportunity to mentor students. Candidates must hold a PhD or anticipate completion of a PhD prior to
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conferred before a petition can be filed or a visa document issued to sponsor the individual. The term of the appointment is limited (see Section 5 of the Postdoc Policy for length of appointment
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, United States of America [map ] Subject Areas: Mathematics / applied mathmetics , Mathematical Sciences , Partial Differential Equations , Statistics Machine Learning Computer Science Appl Deadline: none (posted 2025/08
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sponsor the individual. The term of the appointment is limited (see Section 5 of the Postdoc Policy for length of appointment). The appointment involves substantially full-time research or scholarship, and
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. The Department of Cell Biology is looking for a postdoc candidate to conduct research on tissue morphogenesis using zebrafish as a model system. The candidate will ideally have a training in
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is limited (see Section 5 of the Postdoc Policy for the length of the appointment). The appointment involves substantially full-time research or scholarship and may include teaching responsibilities
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that the requisite degree be conferred before a petition can be filed or a visa document tissued to sponsor the individual. The term of the appointment is limited (see Section 5 of the Postdoc Policy for length
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policies pertaining to other schools at Duke University. The postdoc candidate is expected to: 1) Develop novel methods for incorporating scientific machine learning in solving problems in solid mechanics