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evolution of EO Sentinels data processing algorithms and the management of related procurement activities for their efficient implementation; supporting the operational Copernicus Sentinels missions and data
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scheduling to help make offshore wind farms a reality. Job description This post-doctoral position focuses on developing fundamental algorithmic advances for dynamic planning and scheduling in multi-objective
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algorithms developed for the mission. The aim of this project is to develop and test enhanced L2 algorithms for the four hydrological parameters of HydroGNSS, leveraging a combination of machine learning
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, consisting of methodologists, statisticians, and data scientists, contributes to the development, evaluation, and implementation of state-of-the-art research methods at the intersection of data science, (bio
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website: http://www.esa.int Field(s) of activity for the internship Topic of the internship: Algorithmic Optimisation of Stowage for a Cargo Return Vehicle You will help develop a numerical optimisation
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system-level grid-connected LDES models for grid support Research, design and development of control algorithms for optimal operation of grid-integrated LDES; Develop a co-simulation framework to analyse
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the AI algorithms state of the art for crater detection. generate an overview of available meta data in coordination with the game developers. identify potential use cases in the science community and
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to systematically analyse all trench documentation using data-driven methods to better interpret the reliability of underground maps? Your Role As an EngD candidate, you will work under supervision to develop a data
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-board Payload Signal and Data Processing algorithms and techniques for RF payloads and instruments in close collaboration with TEC-ED; and Time and frequency references, modelling, design tools
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data, complicated algorithms are required. Some of these algorithms are yet to be developed and/or validated, with real data from polar regions required in order to do so. A dedicated airborne radar