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cutting-edge research at the intersection of materials science, AI-informed modeling, and sustainable product development, helping position Maine as a global leader in CNF-based innovation. Typical hiring
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Google Earth Engin, R, Python, and STAN (e.g., deep learning, Bayesian regression models, spatial analyses), and running analyses on a high-performance computing cluster. Demonstrated record of publishing
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Associate in PFAS Predictive Modeling with the University of Maine Cooperative Extension will establish and sustain a dynamic research and educational outreach program to address data and modeling needs
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to offer. Qualifications: Required: PhD in ecology by start date Experience in plant phenology, biogeography, and spatial and temporal modeling (Bayesian and frequentist) Expertise in R or Python, GIS, big
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evolution , social learning, and environmental implications Mathematical or simulation modeling experience Other Information: To be considered for this position you will need to “Apply” and upload