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Field
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) data sets in R or Python as well as GIS software such as QGIS or ArcGIS Pro. Knowledge of US forest ecosystems and background in analyzing forest structure, stand dynamics, biodiversity, and/or other
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software (e.g. ArcGIS, QGIS) and coding environments (e.g. Python or R), collaborating across LUMHR themes, and supporting interdisciplinary research activity. Teaching support may be required, up to a
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programming (R or Python). Advantageous: geostatistics, digital soil mapping, remote sensing, GIS, big data or cloud tools. Proactive working style, strong communication skills, and excellent English. Relevant
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and erosion processes. Skills in GIS, spatial analysis of large scale data, modelling, remote sensing, and programming (R or Python) are desirable. You work independently, communicate effectively
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. Proficiency in at least one of the GIS tools: ArcGIS or QGIS. Proficiency in at least one of the following programming languages: R or Python. Other Requirements or Other Factors: May travel to sites for field
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to mentor student assistants and provide regular constructive feedback toward identified program goals. Willingness to learn more about digital scholarship tools and applications, such as Python, R, GIS, and
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industry-standard frameworks to gather project requirements, write and test software, and generate technical documents. This primary software application includes GIS-based visualizations and extensive
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(R, Python, etc.), and working knowledge of data management protocols. Experience with exposome or environmental data, multiomics integration, and exposure-wide association studies (ExWAS) and GIS
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research environments are essential. Proficiency in ArcPro, Python and a solid grasp of planetary geological processes, especially those relevant to Mercury, will be key to your success in this role
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well as reservoir geology and geophysics in the context of various geoenergy applications (hydrocarbons, geothermal energy, CCS, UHS) Expertise in GIS-based geodata management Expertise in the creation of reservoir