91 phd-mathematical-modelling-population-modelling Postdoctoral research jobs at University of Washington
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, Fisheries Science, Biology, Zoology, Biological Oceanography, Mathematics, Statistics, Computer Science, or related discipline Knowledge of modeling ecosystem and/or social network dynamics Strong
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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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. Currently, a number of mathematical models exist to describe the onset and progression of Alzheimer’s Disease and the effects of anti-amyloid therapies on disease progression. The models are informed by a
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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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bending and trafficking. We utilize a blend of mathematical modeling, genome-editing in human stem cells, and fluorescence microscopy. Our projects aim to elucidate the emergent architectures and modes
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methods to advanced imaging techniques. Currently, a number of mathematical models exist to describe the onset and progression of Alzheimer's Disease and the effects of anti-amyloid therapies on disease
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Position Summary The lab of Dr. Jae-Sung Kim, PhD, in the Department of Surgery at WashU in St. Louis is seeking a Postdoctoral Fellow to join our research team focused on ischemia/reperfusion (I/R
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biology and FLASH/ultra-high dose rate radiotherapy (UHDR-RT). This position offers an exceptional opportunity for a motivated PhD or MD/PhD scientist to advance the biological and translational foundations
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% FTE) postdoctoral scholar with expertise in fire modeling and remotely sensed data to support innovative research. This project will improve our understanding of the conditions under which fuel
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: Using biogeochemical evolutionary models to simulate lifeless and inhabited worlds, and Developing disequilibrium-, redox-, and information-based metrics to understand and quantify the influence of life