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• Demonstrated experience in computational or quantitative research methods. • Strong programming skills in Python. • Experience with high-performance computing, geospatial data, and causal inference methods
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(e.g., CWT, PMF). Strong quantitative and data analysis skills, including proficiency in handling large environmental datasets. Proficiency in geospatial analysis Knowledge of contaminant fate and
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and outstanding education are interlinked and equally valued. We are seeking a Senior Post-Doctoral Researcher to join the Geospatial Machine Learning (Geo-ML) project in the National Centre
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work with existing datasets, including socio-economic, ecological, and geospatial databases that are linked to spatial and environmental data to address a variety of fundamental and applied research
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pipelines and visualization dashboards to communicate results effectively. Familiarity with geospatial data analysis and methods for extracting insights from unstructured data. Job Family Postdoctoral Job
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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 10 hours ago
geospatial data processing and python programming. Candidates should also have knowledge of optical, lidar, and ground penetrating radar sensing systems and understanding of pavement structures and condition
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, crop yield). -Familiarity with geospatial data and tools (e.g., GIS, QGIS, Google Earth Engine). -Knowledge of explainable AI (e.g., SHAP, LIME), model interpretation, and/or uncertainty quantification
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Basic proficiency related to geospatial analysis (e.g., ArcGIS, QGIS, Google Earth Engine). Strong oral and written communication skills. Experience working with and managing interdisciplinary research
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Earth Observation data including Satellite Remote Sensing together with Soil and geospatial datasets. The All-Island Climate and Biodiversity Research Network (AICBRN) brings together researchers from a