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with Indigenous and/or rural communities. Experience in community engagement Knowledge and awareness of Indigenous and rural health and Indigenous health research ethics. Statistical analysis, geospatial
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utility-facing geospatial toolkit through data science and partnerships with grid operators. Duties and Responsibilities ● Develop a scalable data science pipeline to harmonize and link detailed information
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, Biology, Remote Sensing, Geography, or a related field by the start of appointment Demonstrated experience conducting quantitative research with environmental, ecological, or geospatial datasets Knowledge
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chemical transport model (CTM). These geospatial inputs and information will be used to fine-tune a NASA foundation model (Prithvi WxC) to emulate CTM processes, and to predict ground-level air quality data
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
, this will include the refinement of clinical research data models, development of ingest and QA/QC pipelines, and support for geospatial data visualization. The work will be performed in several cloud and/or
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and heterogeneous datasets, which may include climate projections, electricity-infrastructure records, solar-resource data, energy-demand data, urban geospatial data, economic input–output tables, and
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Experience in working with large geospatial datasets (e.g., ERA5, CMIP6, Sentinel, MODIS) Working with complex models and high performance computing, ideally dynamic global vegetation models (DGVMs
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, representation learning) Spatiotemporal modeling or geospatial/temporal data analysis Medium-to-Large-scale foundation models pretraining/fine-tuning paradigms Strong programming skills in Python and experience
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. The selected candidate is expected to have expertise in one or more of the following areas: modeling contaminant flow and transport at various geospatial scales, process-based modeling of soil organic matter
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Python, R or a similar scientific programming language, with a focus on reproducibility and open-source best practices Demonstrated experience in geospatial data analysis and the management of large