55 phd-in-mathematical-modelling-of-biochemical-reactions Postdoctoral research jobs at Duke University
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uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments. • Collaborate with mathematical modelers and experimentalists in
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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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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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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control
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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
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pregnancy and early childhood. The project integrates exposure modeling, biomonitoring, and immune profiling to assess early-life susceptibility and long-term health impacts. The Scholar will work closely
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outside Duke University. Preferred qualifications: PhD (completed in the last 1-5 years or PhD candidate) in a quantitative discipline, including Computational Biology, Bioinformatics, Computer Science
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by two NIDA-funded grants. The first project, NEURONIC, utilizes a nasal spray paradigm to assess reactions to nicotine in a controlled, laboratory setting as a model of risk for addiction
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pathways and mechanisms underlying autoimmunity from a lncRNA and epigenetic gene regulation perspective. We utilize biochemical assays, tissue culture, mouse transplantation & disease modeling experiments
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model over the Contiguous United States, and evaluate model deficiencies and model improvements to improve the modeling of spatial heterogeneity of LST in land surface models. In Addition, Will Also