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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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. The project goals are to evaluate the geochemical characteristics of acid mine drainage (AMD) fluids and treatment solids at sites that are known to be enriched in rare earth elements, cobalt, and other metals
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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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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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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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will develop novel statistical and machine learning methods for any of the following: multi-omics data (such as bulk and large-scale single-cell RNA sequencing data, spatial transcriptomics, bulk and
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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
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with various methods that can incorporate domain-based constraints and other types of domain knowledge into machine learning and applying these techniques to problems in computational creativity
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for a Postdoctoral Associate. The lab utilizes contemporary methods in molecular genetics, genomics, cell biology, biochemistry, bioinformatics and microbiology to answer basic and translational questions