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reviews; consolidating and maintaining the requirements specification, design, development, testing and verification of satellite/platform subsystems and equipment; monitoring industrial work for the
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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notices; monitoring the progress of tool development and providing regular status updates to the project management team; facilitating the use of collaborative tools for reporting and tracking the status
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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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of the Section features an anechoic chamber for conducted and radiated EMC testing, complemented by several facilities for DC and AC magnetic cleanliness tests. In addition to laboratory testing, the Section
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towards a future-proof logistics system with a special focus on machine learning-based collaborative scheduling, resource sharing, and self-organisation. The EngD position corresponds to a 2-year post
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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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-term needs. fostering new application areas for multidisciplinary activities, placing emphasis on innovative concepts, cutting-edge technologies and system architectures. monitoring applicable scientific
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of Resources and Services. This will include responsibility for monitoring the standard of work and proactively liaising with the BUC’s Office when issues arise to ensure that the required improvements
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the field of radiation protection and monitoring; monitoring applicable scientific and technological trends and maintaining state-of-the-art expertise; contributing to the dissemination of the results