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of the position is to monitor the conditions of crop, pasture, and their growth environment to support agricultural decision making with advanced remote sensing and geospatial technologies. Responsibilities
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observed pipe failures, and interpreting indirect observations from remote sensing platforms such as drones and satellites. Another challenge is linking these observations to physical processes affecting
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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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geographic information systems, cartography, and remote sensing. Students acquire theoretical and practical knowledge, methodologies, and techniques for solving geographical problems. It encompasses scientific
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solutions for vibration and noise control in lightweight structures (https://cordis.europa.eu/project/id/101227712 ). The project focuses on the development of Acoustic Black Hole (ABH) technologies
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assessment criteria Experience with programming (e.g. Python, MATLAB, Fortran or similar), numerical modelling, remote sensing, large datasets, or fieldwork is considered meritorious. Employment process
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this framework, we are looking for a motivated PhD researcher who will work across KU Leuven and the University of Essex, at the interface of urban ecology, remote sensing, environmental epidemiology and data
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environment to support agricultural decision making with advanced remote sensing and geospatial technologies. Responsibilities: Develops advanced Agro-geoinformatic algorithms for monitoring and predicting
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spaceborne remote sensing. You will first identify large-scale drivers of compound extremes in models and observations, then build an emulator using advanced AI methods, such as convolutional neural networks
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
the surface properties of the Asal-Ghoubbet rift by massive inversion of the Hapke model on Pleiades multiangular images. Remote Sensing of Environment 322, 114691. https://doi.org/10.1016/j.rse