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The Department of geosciences and natural resource management offers a 19-month Postdoctoral position in the field of Deep Learning and Remote Sensing, with a specific focus on developing, training
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the field to inform farmers of varying soil conditions through traditional remote sensing techniques. For further Information, please email p.stowell@sheffield.ac.uk or look at the University’s website: https
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relevant to the position. Research Fellow I: PhD from an accredited institution in Agronomy, Crop Science, Soil Science, Agricultural Engineering, Data Science, Remote Sensing, or a closely related field; no
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] Y. Ren, B. Liu, B. Liu, Z. Liu, P. Jiang, Joint Inversion of Seismic and Resistivity Data Powered by Deep Learning, IEEE Transactions on Geoscience and Remote Sensing, 62, 5929614, 2024. https
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interpretation, and the dissemination of research outcomes through scientific publications and project reports. PhD in computer science, artificial intelligence, computer vision, image processing, remote sensing
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to secure long-term project financing. Sector Collaboration: Collaborate closely with partners in the Swedish forest sector to ensure research relevance. Your profile Required: A PhD in Forest Remote Sensing
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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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seagrass data Knowledge of GIS, remote sensing or ecological modelling Experience in linking scientific research with environmental policies Scuba diving certification at the rescue diver level Ability
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biogeochemical cycling, individual PhD projects can involve aspects of (geo-)microbiology, soil science, atmospheric measurement techniques, atmospheric transport models, terrestrial and atmospheric remote sensing
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 22 days 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