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: simulating carbon reduction potential at parcel level under climate change scenarios across Europe; design and implement geospatial hybrid and mechanistic modelling frameworks using process-informed machine
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: 12 September 2025 Apply now Are you a data scientist interested in designing and implementing process-informed machine learning and uncertainties quantification methods? Join us as a postdoc and work
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should have: Demonstrated experience with computational fluid dynamics (CFD) is required. Experience with computer aided design (CAD) tools would be an asset. Experience with programming and scripting
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: simulating carbon reduction potential at parcel level under climate change scenarios across Europe; design and implement geospatial hybrid and mechanistic modelling frameworks using process-informed machine
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Introduction Are you interested in contributing to the future of regenerative farming, food systems and finance? Do you want to explore how the concept of integrated value (‘brede waarde’) can help transform
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August 2025 Apply now Machine Learning models are increasingly important in the atmospheric sciences. After training, they can emulate model outcomes at a fraction of the computational cost of traditional
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conventional, frame-based sensors in comparison to event-based sensors, which are more commonly associated with neuromorphic computing. This work will support mission design decisions regarding the suitability
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Systems Engineering & Innovation Department, Directorate of Operations at the European Space Operations Centre (ESOC). The Section is responsible for driving cyber security research and development applied
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systems, design concepts, lean development approaches, for example Agile, and working methods. Through its daily work, the Team acts as a pathfinder for the low-cost, green launch systems of the future
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including interplanetary cruise, aero assistance, precision landing, ascent, rendezvous and docking, re-entry, formation flying and drag-free systems. Special emphasis is placed on the following areas