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Field
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that are derived from novel ground-based atmospheric remote sensing profile observations. Heat risks to human health and mortality are exacerbated in urban areas, where heat waves are more intense and last longer
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of surface-atmosphere interactions and limitations in current observational methods . Traditional remote sensing techniques are generally indirect, inferring evaporation from thermal imagery and reflectance
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processes, remote sensing observations, and prediction and valuation of forest functions, providing a holistic view on forests as complex systems. See the project website to find out more about the FORFUS
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, and will work in close collaboration with the FOX team of the CRISTAL laboratory. The LOA team has internationally recognized expertise in the field of radiative transfer and remote sensing. The CRISTAL
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engineering/science, remote sensing, computer science). A relevant master’s degree and/or employment experience will be an advantage. English language requirements: Applicants must meet the minimum English language
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English Strong independence, commitment, and sense of responsibility Reliable and conscientious working style Excellent teamwork and collaboration skills Our Offer: We work on the very latest issues
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such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary geospatial or remotely sensed data. Quantifying uncertainty and correcting
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Dr Marc Russwurm. Your qualities We seek a highly motivated PhD candidate having: a master’s degree in Geo-Information Science, Physical Geography, Remote Sensing, Applied Mathematics, Statistics or a
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impact and adaptation analyses, and for spatio-temporal modelling as well as upscaling of ecosystem properties via remote sensing Interactive collaboration and exchange within the TUM Center for Forest
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AI & Cyber Futures Institute - Charles Sturt University | Bathurst, New South Wales | Australia | about 1 month ago
technologies or IoT being advantageous. A background/knowledge in agriculture relevant to project focus will be desirable. Familiarity with digital twin modelling, remote sensing, and cloud computing is valued