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model biases, and identify sources of predictability. The project will involve; 1) rigorous interrogation of NOAA GFDL's CM4X simulation output with respect to coastal sea level variability and relevant
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to predict nitrogen (N) and phosphorous (P) excretion, and this was published by Fox et al. (2004). Further, those predictions were refined and improved and partition N and P excretion between urine and feces
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at the University of Utah (https://www.boschlab.com/) invites applications for a computational post-doctoral position to support projects related to biomolecular interaction prediction using AI tools. The Bosch Lab
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prediction Risk assessment and catastrophe modeling High-performance computing and large dataset manipulation The position involves close collaboration with industry partners in climate risk assessment
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University (ASU) invites applications for two postdoctoral scholar positions in the area of mathematical modeling to work with Dr. Nina Fefferman. The first position will focus on modeling dynamics
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the development of predictive fisheries and bioeconomic models to estimate outcomes, including but not limited to fisheries spillover effects and biodiversity benefits, across global and regional networks of MPAs
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. The postdoctoral scholar will contribute to an exciting research program within the Foy Lab (https://foylab.xyz/ ), focused on developing computational methods to enhance how we collect, analyze and use clinical
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for transportation prediction, system optimization, and environmental/health impact modeling Deployment of decision-support tools for public-sector clients (municipalities, MPOs, DOTs) Urban mobility, equity
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, to define novel biomarkers, and to identify novel therapeutical targets. We have pioneered in the integration of genetics with omic data to identify proteomic signatures and develop novel predictive models
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. The individual is required to use tools in chemical ecology, genomics, molecular biology, and AI-driven predictive modeling in the research project. Specific duties will include: (1) gather and merge existing data