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curated pharmaceutical science and public health databases that can be used for modeling purposes. Information about Dr. Zhu’s research and recent publications can be found on his lab website: https
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: www.zhuhlab.com The candidates will focus on one or two of the following projects: Developing predictive toxicity models for assessing new environmental pollutants, nanoplastics, and environmental mixtures
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: www.zhulab.com The candidates will focus on one or two of the following projects: Developing predictive toxicity models for assessing new environmental pollutants, nanoplastics, and environmental mixtures
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independent research aligned with the aims of the Addiction & Decision Neuroscience Lab (ADN). Current work focuses on cognitive modeling of decision-making in both laboratory tasks and real-world settings, as
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within the School of Engineering. Among the key duties of the position are the following: Leverages advanced AI/ML to build predictive models for the environmental impacts of hydroclimate extremes, such as
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aligned with the aims of the Addiction & Decision Neuroscience Lab (ADN). Current work focuses on cognitive modeling of decision-making in both laboratory tasks and real-world settings, as well as machine
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biomarker data. Develop predictive models and algorithms to identify risk factors, disease markers, and potential therapeutic targets for Alzheimer’s disease. Implement machine learning models to improve
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geophysical monitoring, data integration, and the development of predictive models and visualization tools. Candidates should have a PhD in geophysics, hydrology, environmental engineering, or a related
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. Strong written, verbal and presentation communication skills. Preferred Qualifications Doctoral degree. Demonstrated experience leveraging data analytics, artificial intelligence (AI), predictive modeling
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techniques to develop explanatory or predictive models for spatial datasets. Equipment Utilized ESRI ArcGIS software platform, Microsoft Office (Word, PowerPoint, Excel, and work/team management software