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, to evaluate the potential population count and distribution of beavers. - Use/develop appropriate computer modeling and remote sensing techniques to estimate vegetation greenness, burn severity, and total water
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an individual with strong theoretical and modeling skills, with a strong background in using satellite data to construct predictive models that allow the early detection of tree diseases from space. The research
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faculty in developing theory and application tools for artificial intelligence (AI), and training efficient data analytics. 60% - Leading research in AI will include generative models, algorithms and
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flux measurements using biogeochemical modeling. They will be responsible for managing projects related to field instrumentation and ecosystem flux modeling. 20% designing and implementing trace gas flux
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the pathologic mechanisms for Alzheimer’s Disease (AD) and Parkinson’s Disease (PD) using cellular and transgenic mouse models. The candidate will assist the Principal Investigator by performing duties related
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, computer science, or related field 6 years of experience in compensation modeling, with at least 2 years experience with faculty physician compensation models 3 years of experience managing large databases with
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quality models to better understand the effect of soil health management practices on soil-water storage in agricultural fields. A postdoctoral position is available to examine the effect of soil health
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comparative pathology and animal models of aging, and exposure to research on the biology of aging and at the interface of aging with cancer. After completion of the residency program, qualified individuals
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scales, to integrate data science, bioinformatics, computational modeling, and machine learning, and help create sustainable solutions to improve decision-making. Areas of research may include quantifying
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., hidden Markov Model or transformer. (ii) Basic statistical methods, e.g., parametric/nonparametric hypothesis testing or bootstrapping. 2) Large, complex data analysis: (i) High-performance computing