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learning, allowing rapid but rigorous system architecture definition of a launch vehicle within the MBSE collaborative environment. You will also carry out research in the field of Uncertainty Quantification
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from resources like Wikidata or fan wikis. In addition, both the full text and the existing triples can be leveraged for the extension of the knowledge graph via automated reasoning, inferential learning
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records from satellite data, and/or improved methods of uncertainty characterisation, including the use of artificial intelligence and machine learning to improve or analyse satellite climate data records
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in combination with other machine learning techniques, to create predictive models. You will engage in an interactive feedback loop with domain experts to analyze discovered models and remove any
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or geoinformatics) and the willingness to acquire the others during the first months of the project will suffice to be taken into consideration. Our offer We offer: a position for up to 34 months - the length
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better world for people and planet. Together we create a safe and respectful working and study climate, and an inspiring environment for education and research. Learn more about our codes of conduct We
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closely with experienced faculty members to develop and deliver courses at the BSc and MSc levels. RSM has a dedicated Learning Innovation Team (LIT), which supports faculty members in the improvement and
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develop a simplified model focusing on the leader stage. You will: Analyze experimental data and microscopic simulations Identify relevant physical features and parameters Apply machine learning techniques