92 phd-in-mathematical-modelling-population Postdoctoral positions at University of Washington
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Position Summary A Mathematical Epidemiology/Infectious Disease Modeling position is available in the Mitreva Lab with the Division of Infectious Diseases, Department of Medicine and McDonnell
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uses various non‑model species and a hypothesis driven, systems approach to examine how these factors impact plant defense. We explore how plants have navigated the fine line between growth and defense
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, Biological Oceanography, Mathematics, Statistics, Computer Science, or related discipline Knowledge of modeling ecosystem and/or social network dynamics Strong quantitative skills Proficiency with statistics
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the following fields is required: mathematical or computational biology, mathematical/statistical modeling, optimization, pharmacokinetics/pharmacodynamics (PK/PD) modeling, pharmacology, or related
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dementias. We use a combination of iPSC-based systems and transgenic mouse models coupled with novel approaches including single-cell sequencing, CRISPR–Cas9 screening, and interactome profiling. The work
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Position Summary A Postdoctoral Research Associate position is now open in the Puram Lab (PI: Sidharth Puram, MD PhD) at WashU Medicine in St. Louis, Missouri, integrated into the Departments
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(Podostemaceae) in Neotropical rivers migrated and evolved as the Isthmus of Panama formed and develop the first model for the tempo and pattern of formation of riverine connections across the Isthmus
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Labor Relations website . The salary range of this position is $6,681 to $7,500 per month ($80,172 – $90,000 per 12-month academic year). Qualifications Applicants must have a PhD, JD, EdD, or foreign
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measurement. Additional duties involve microbial population analysis (16sRNA), chemistry analysis (N, P, metals), and statistical modeling. The role also entails lab management, including cleaning and safety
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Position Summary This position will focus on integrating high-resolution field monitoring, remote sensing, and statistical and numerical modeling approaches to improve predictive flood hazard