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system, consisting of several components, for processing quantum algorithms, tailored to meet user needs. Developing and testing tooling software, ensuring quality through unit and integration testing
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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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University Medical Center Utrecht (UMC Utrecht); Published yesterday | Netherlands | about 21 hours ago
, 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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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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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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is the co-Principal Investigator Institute for TROPOMI, a Dutch instrument realised together with ESA and launched October 2017. SRON’s responsibilities include the development and maintenance
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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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-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
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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