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methods to evaluate the UFS ‘s ability to predict extreme events. The purpose of the project is to evaluate the biases and skill of the sub-seasonal to seasonal forecasts generated with the UFS prototypes
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Intelligence/Machine Learning (AI/ML) methods in agriculture (Agro-AI/ML); and Experience in programming with multiple languages (e.g., Java, C/C++, Python) for geospatial information systems, agro-informatic
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to be named the Department of Health Administration, Policy, and Informatics (HAPI) and hosts two PhD programs in Health Services Research and Health Informatics. The initial appointment is for a
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and outreach activities. Required Qualifications: Terminal degree in a related field; Relevant research experience either through PhD work or through other research projects; Knowledge of mathematics
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laboratory; and Participates in relevant professional development opportunities to further support career development. Required Qualifications: Terminal degree in a related field; PhD in Geology or related
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models to characterize agricultural and ecological systems; Experience in applying advanced Artificial Intelligence/Machine Learning (AI/ML) methods in agriculture (Agro-AI/ML); and Experience in