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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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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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that enhance the development and evaluation of advanced analytical models using health data. This includes methods for prediction, explainability, prediction under intervention, algorithmic fairness, transparent
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for prediction, explainability, prediction under intervention, algorithmic fairness, transparent model validation, and post-deployment quality control. You will also lead the implementation of the Data Science
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the Search and Rescue (SAR) test campaign with the available Medium Earth Orbit Local User Terminal (MEOLUT) deployed at ESA ESTEC, debugging and updating it with newest or R&D SAR features algorithms
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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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algorithms for optimal operation of grid-integrated LDES; Develop a co-simulation framework to analyse LDES performance under different grid scenarios. Collaborate with consortium partners to translate
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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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-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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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