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remote sensing data. Your job As a PhD candidate, you will study how the rapid thinning and loss of sea ice in the Arctic Last Ice Area, north of Greenland and Ellesmere Island, affects surface melt and
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and remote sensing data including leading disaster event response, coordinating data collection and field campaigns, and supporting data integration efforts across the Center's four Hazard Observatories
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Required qualifications PhD within forest sciences, remote sensing, data science or related field Competence in data processing and analysis of remotely sensed data Experience in use of remotely sensed data
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a candidate with: A PhD degree in Hydraulic Engineering, Marine Engineering, Earth system Science or related discipline. Experience with physical (wave) modelling is required. Being available at short
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postcolonial studies Environmental Studies Environmental and Climate Science Environmental Health and Risk Assessment Ecology and Conservation Science Environmental Policy and Governance GIS, Remote Sensing, and
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Instituto de Ciencias del Patrimonio - Spanish Council for Scientific Research (CSIC) | Spain | about 2 months ago
self-generated data in the field-, and to explore the potential of remote sensing to unlock the relationship between vegetation history and the distribution of ancient economic and settlement remains
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. Demonstrated proficiency in GIS, remote sensing, spatial analysis, or other applied geographic techniques. Preferred: PhD degree in Geography, Geosciences, Computer Science, Environmental Science, or a closely
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growing regions of Kenya. About You You will be educated to doctoral level in some aspect of remote sensing, agricultural engineering, geospatial science, or a closely related field. Ideally you will have
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of remote sensing application to quantify carbon sequestration is desirable Proficiency in written and spoken English Where to apply Website https://www.timeshighereducation.com/unijobs/listing/404286/asari
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of coding (R and/or Python), enthusiasm for quantitative analysis Desirable: GIS/remote sensing, bioacoustics, machine learning, or statistical ecology. Field experience is useful but not required. Impact and