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of a model-based digital twin to be applied to cryogenic liquid propulsion systems and their main components using innovative techniques such as chemical reactor networks or surrogate models for machine
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of Amsterdam. Interested in developing fundamental machine learning techniques for tabular data to democratize insights from high-value structured data? Then this fully-funded 4-year PhD position starting Fall
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observations. Your major challenge is in model development, and there is room for you to develop machine learning applications in the field of firn modelling. If successful, your work will lay the foundation
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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
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intelligence, for example for machine learning and predictive maintenance and on-board and on-ground flight data processing; developing an artificial intelligence strategy for European launcher manufacturing
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technologies, for example, using machine learning techniques to support long term exploration; Topics related to ‘off world living’, e.g. human factors, design and concept illustration; Crew Health and
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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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data is lacking. With the DataLibra project, we aim to close this gap, by developing AI models and tools for structured data (Table Representation Learning), to help organizations, of any size, domain
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or showing willingness to learn machine learning models Having interest in stakeholder engagement Desired knowledge of Nature-based Solutions, especially in agriculture and developing country contexts
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described in the project overview. Owing to the current composition of the project team, there will be a mild preference for candidates opting for project 2 on “Models and machine learning”. An explanation