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or acquire further expertise in quantitative intravital microscopy. 2) Computational or Cell biologists (with expertise in quantitative microscopy, statistical modeling, cell culture, and/or biochemistry
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continued funding Responsibilities: Under the direction of the Principal Investigators, the Associate will contribute to empirical modeling of the economic and security effects of climate change. In
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epilepsy patients and non-human primates are conducted using identical behavioral paradigms and combined with computational approaches. We are seeking an extremely motivated postdoctoral researcher with
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
The Atmospheric and Oceanic Sciences Program at Princeton University, in association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks a postdoctoral or more senior research scientist
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health services to Princeton University faculty, staff, and employees. An integrated, evidence-informed model guides all UHS practices and services. UHS leverages clinical encounters and prevention efforts
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background in chemical and biological engineering, bio-engineering, molecular biology, microbiology, biochemistry, biophysics, computational modeling or related fields. Experience in metabolic engineering
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University to merge CFPS data with granular COVID mitigation policy data and specify models for evaluation of policy effects. *Communicate with the director of the secure data enclave at Peking
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to ion beams with well-controlled energies and incident angles for benchmarking and validation of theoretical calculations and computational physics and chemistry modeling of important surface processes
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/or energy *Strong methodological and quantitative skills, such as survey and sampling design and data analysis (in R or Python), meta-analysis and/or document/text analysis, or computational modeling
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computational modeling techniques to study planning in rodents engaged in dynamic spatial foraging tasks. The successful candidate will develop computational models of reinforcement learning in the brain and