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Field
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Geospatial Data Sciences. Faculty members are actively engaged in research spanning three key themes: Geospatial Data Sciences (e.g., remote sensing and Geographic Information Science [GIS]), Environmental
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, computationally efficient modeling techniques, and critical evaluation of highly multidimensional model outputs. Experience with small-area or geospatial epidemiologic models. Ability to work both independently and
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offers comprehensive bachelor's, master's, and doctoral programmes and is experiencing strong growth, including the recent launch of a Master of Science in Geospatial Data Sciences. Faculty members
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experiencing strong growth, including the recent launch of a Master of Science in Geospatial Data Sciences. Faculty members are actively engaged in research spanning three key themes: Geospatial Data Sciences
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. Experience and expertise in human mobility simulation and prediction with agent-based modeling and deep learning techniques. Proficient in Python programming for geospatial data processing, modeling, and
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are uncertain, and decisions about where to survey unfold sequentially under significant time and cost constraints. Existing predictive models provide useful but incomplete support because they cannot fully
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methods, with a focus on geospatial analysis. The Calendar Year (CY) Associate position will be awarded for a one-year period beginning in September or October 2026, with the possibility of renewal based
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, with a strong interest in the integration of geospatial Artificial Intelligence (AI) and machine learning. Are you enthusiastic about the chance to combine research in Remote Sensing and AI with teaching
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Multiple Research-Intensive Associate/Full Professor Tenure System Positions & an 1855 Professorship
, implementation science, geospatial analysis, biostatistics and research design, analysis of interventions (e.g., difference-in-differences), AI analytics, agent-based modeling, Bayesian modeling, causal inference
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. Existing predictive models provide useful but incomplete support because they cannot fully capture the complexity and heterogeneity of archaeological data. This underscores the need for approaches