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new NIH-funded Center for Excellence in Multiscale Immune Systems Modeling. This position focuses on leveraging and developing new equation learning methods, such as Physics-Informed Neural Networks
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design and management; mentorship and coordination skills; familiarity with plant ecophysiology lab methods. Position details: • Start date: Flexible, as early as August 2026 • Location: Durham, North
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Systems Modeling. This position focuses on leveraging and developing new equation learning methods, such as Physics-Informed Neural Networks (PINNs), Biologically Informed Neural Networks (BINNs), and
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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
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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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evaluation methods, discrete choice experiments, systematic reviews, and meta-analysis, with high levels of proficiency in associated software (e.g., Stata, R, Ngene). Applicants should have knowledge
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using advanced analytical chemistry methods in the EBCL. The work will focus on targeted molecular analysis using mass spectrometry. Choose Duke. Join our award-winning team and be part of an inclusive
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research and handling sensitive data is preferred. • Strong quantitative skills, including proficiency in regression modeling, environmental mixtures analysis, and spatial methods using R. • Familiarity with
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applications in environmental health and ecology as well as working with motivating datasets to become familiar with the data structure, challenges and competing methods. This position requires a Phd degree in
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innovative statistical methods in clinical trial design and variable selection methods in high dimensional data that will predict clinical outcomes and meta-analyses. The successful candidate will collaborate