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Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically
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of the research project, research may also incorporate advanced optical diagnostics, quantitative image analysis, computational modeling, and remote sensing technologies to improve understanding, evaluation, and
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underlying genetic components, helping researchers better understand how plant genetics and fiber quality interact. You will engage in a collaborative, multidisciplinary research program spanning chemistry
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collecting soil, plant, and water samples; monitoring soil moisture with advanced sensors; organizing and processing research data; and contributing to modeling efforts that examine water and nutrient dynamics
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security threats. The CDC IPP regulates the importation of infectious biological materials that could cause disease in humans to prevent their introduction and spread into the U.S. The program ensures
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Must be a U.S. Citizen Preferred Skills: Experience with MATLAB Familiarity with emerging technologies including instrumentation, computer modeling & simulation (e.g. Matlab, CFD, and/or other), and the
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join a community of scientists and researchers in an effort to research modeling approaches related to neural stimulation and inhibition by laser exposure. Research will primarily focus on
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. This creates large uncertainties when modelling orchard water and nutrient fluxes as well as deep percolation below the root zone, critical topics in semi-arid, agriculturally productive regions such as
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managing diabetes using computer simulation models. Learning Objectives: You will learn: How to synthesize and translate empirical evidence on cost-effectiveness of interventions for the prevention and
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with these plants using in vitro and in vivo animal models. Animal tissue and cell culture models will be used to study the ability of compounds of interest to cross absorptive barriers as