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of vegetation health. Translate insights gained to the exploitation of current radar satellite missions. You will be supervised by Professor Susan Steele-Dunne, an expert in microwave remote sensing of vegetation
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. Translate insights gained to the exploitation of current radar satellite missions. You will be supervised by Professor Susan Steele-Dunne, an expert in microwave remote sensing of vegetation. You will be part
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using remotely sensed data Apply for this job See advertisement About the position Faculty of Environmental Sciences and Natural Resource Management (MINA) at Norwegian University of Life Sciences (NMBU
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Helmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum GFZ | Potsdam, Brandenburg | Germany | 25 days ago
Preparation of remotely sensed observations, e.g., from GRACE and Swarm satellites Assimilation of gravimetry and electromagnetic observations Analysis of modelled and state estimated geophysical systems
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PhD Supervisor: Adriana Wilde Supervisory Team: Dr Adriana Wilde Project description Experience with us the cutting edge of unobtrusive remote sensing and its integration into our daily lives
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how clouds impact the surface mass balance of the Greenland Ice sheet. This challenging research position requires developing a good understanding of climate modelling and remote sensing. You will work
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. Requirements Specific Requirements An MSc in remote sensing of water quality, or related field Strong understanding of radiative transfer processes within the visible part of the solar spectrum, coupled with a
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Framework Programme? Not funded by an EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD student in Earth Sciences Specialization in remote sensing
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Published: 2024-01-19 PhD student in Earth Sciences Specialization in remote sensing and snow physics Are you interested in working with remote sensing and snow physics, with the support of
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the 3D Discrete Anisotropic Radiative Transfer (DART) model to disentangle the functional (photosynthetic) and structural (architectural) contributions to forest canopy SIF derived from remote sensing