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, MODFLOW, SWAT, PHREEQC), geographic information systems (ArcGIS, QGIS), remote sensing, and scientific writing. Excellent communication skills in English (spoken and written). Ability to work independently
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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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species, and the emergence of previously unseen classes. Recent advances in remote sensing and machine learning provide new opportunities to address these challenges, but most current approaches
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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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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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geospatial or remotely sensed data. Quantifying uncertainty and correcting for spatial and sampling biases inherent in environmental observation systems. Target environmental properties such as above ground
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Job Purpose The successful candidate will contribute to a project focusing on remote RF Sensing and its applications in healthcare involving technologies for both sensing and communications, under
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, 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 doctoral