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and initiatives in the field; participate in internal research sprints to explore, test and validate novel EO concepts, algorithms and workflows in a fast-paced, collaborative environment; support the Φ
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intelligence (AI) and machine learning(ML). Duties This position combines knowledge of the Earth observation (EO) domain (EO instruments, EO data, EO algorithms, modelling, etc.) and AI/ML, as well as data
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strategy; participate in internal research sprints to explore, test and validate novel EO concepts, algorithms and workflows in a fast-paced, collaborative environment; engage with the innovation ecosystem
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performance shall be continuously monitored during this phase to confirm the configuration of the system algorithms. The EGNOS System performance analysis environment is composed of a set of tools, processing
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from multispectral datasets You will contribute to the ongoing development of Machine Learning algorithms for recognition of planetary materials from multispectral datasets. This project combines deep