69 phd-position-in-data-modeling-"UCL"-"UCL" Postdoctoral positions at Duke University in United States
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Duke University, Gender, Sexuality and Feminist Studies Position ID: Duke-Gender, Sexuality and Feminist Studies-POSTDOC [#30734] Position Title: Position Type: Postdoctoral Position Location
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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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. Lead a research project on tissue morphogenesis using zebrafish as a model system. Design and conduct experiments by implementing all the necessary step for data generation. Perform data analysis and
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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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ongoing and planned experiments. This position is on-site. Projects include: · Establishment of a human stem-cell derived endometrial organoid model for use in co-culture to study pre-eclampsia and related
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Duke University, Mechanical Engineering and Materials Science Position ID: Duke -MEMS -PDAQUINO [#30494] Position Title: Position Type: Postdoctoral Position Location: Durham, North Carolina 27701
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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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Duke University, Office of the Dean, Pratt School of Engineering Position ID: Duke -FacultyResearchMento -POSTDOCCHANEY [#30264] Position Title: Position Type: Postdoctoral Position Location
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Activities: Research Topic: Bioengineered Human Tissue Model for Juvenile Dermatomyositis Job Description: Our lab is focused on using bioengineered human muscle systems (myobundles) to research rare pediatric
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data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental