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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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model the remarkable learning efficiency of the human visual system. The project is an interdisciplinary collaboration between the the Machine Learning group at CWI in Amsterdam (Prof.dr Sander Bohte) and
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., machine learning, stochastic dynamic programming, simulation). Affinity with (food) supply chain management is preferred. To collaborate with and to co-supervise MSc thesis students and internship students
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GIS and spatial analysis Data science, data mining, and information retrieval (multi-modal) Machine learning, deep learning and artificial intelligence (Initial) Experience in grant acquisition and
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data analytics, artificial intelligence and machine learning and statistical and analytical methods and tools Strong understanding of the entire product lifecycle, including the feedback loop
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modelling of materials and machine learning. Experience in atomistic modelling (molecular dynamics, density functional theory) and machine learning is important, as well as a strong interest in pursuing
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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