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or Classics). Equipped with knowledge or practical experience of agentic AI or AI-assisted workflows, they will have experience of Digital Humanities research and of working with geospatial data and ArcGIS, as
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standardization; knowledge of geospatial analysis (GIS, QGIS, Google Earth Engine); experience with carbon and soil health metrics and assessment frameworks; previous publications on soil health and soil carbon
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, and modeling. Geospatial modeling To model the spatial distribution and quantify the suitability of waterfowl habitat conservation capital and opportunity in Michigan, including consideration and
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interpretation of environmental and geospatial data (including GIS and/or drone-based methods), and support its translation into innovative, publicly engaged outputs within the EASE framework. The role includes
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fields: palaeoecological data synthesis and resilience to fire (Dr Jessie Woodbridge), geospatial analysis and archaeology (Prof Ralph Fyfe), palaeo-fire, FTIR and landscape modelling (Dr Michela Mariani
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, maintaining, and documenting open-source software tools or R packages Experience with geospatial analysis, environmental databases, and cloud or high-performance computing workflows Demonstrated record of peer
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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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Postdoctoral Research Associate position. The successful candidate will hold a Ph.D. in marine biology or a closely related field and will have experience in marine geospatial analytics, remote sensing, machine
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. The successful candidate will also apply statistical modeling for mechanistic inference and conduct biogeographic and geospatial analyses of microbiomes. This position involves collaborating with scientists and
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Sentinel-1/2 time series. Strong proficiency in Python for geospatial data analysis and machine learning (e.g. XGBoost), including model interpretation techniques (e.g. SHAP). Very good oral and written