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Field
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leader in the research project - “Remote sensing of urban climate, ecosystem, environment and GeoAI”. Qualifications Applicants should have a doctoral degree or an equivalent qualification in Remote
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challenges faced by Alabama and the world. AWI-affiliated researchers specialize in water security and quality, remote sensing, hydrologic and hydraulic modeling, and watershed management through synergies
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interdisciplinary programs such as Bass Connections. Be Bold. PhD (completed or near completion) in environmental or earth science, epidemiology, remote sensing, geography, global/public health, or a related field
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following areas: plant biology and spectroscopy, evolutionary biology, bioinformatics, biochemistry, functional genetics, plant physiological ecology and remote sensing. All candidates must have received a
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visualization, geospatial analysis, and processing of large amounts of meteorological and remote sensing data. Background Investigation Statement: Prior to hiring, the final candidate(s) must successfully pass a
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the research projects - “Monitoring and predicting slope instability in forested terrain from multi-mode remote sensing data” and “Resilience of rural infrastructure and communities to climate change
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, or environmental sciences with a focus on wetland/peatland ecology, climate science, ecological restoration and knowledge of greenhouse gas, ecohydrology, biogeochemistry, or remote sensing is preferred. Recent
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(pro rata on a part time basis) Apply by: 22/06/2025 Role Description We welcome applications from passionate plant science researchers, bioinformaticians or remote sensing/data scientists with skills in
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passionate plant science researchers, bioinformaticians or remote sensing/data scientists with skills in image processing or phenotyping with a collegiate and self-driven attitude towards multidisciplinary
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will have or be close to obtaining a PhD in a relevant subject such as remote sensing, physics, mathematics, computer science or environmental science. Experience of processing satellite datasets