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on the theory of deep learning will investigate questions such as the structure and expressivity of emerging neural architectures relevant to space, such as implicit neural fields, continuous normalising
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that autonomously optimise EO systems tasking and data collection, using decision intelligence to make real-time decisions based on environmental signals and predictive models; contribute to the rapid prototyping and
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structures, materials and control systems function, and to turn these into validated, reusable abstractions for space engineering. In line with this agenda, the research will make use of modern simulation and
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ownership of a research line in applied mathematics and advanced numerics, with a strong emphasis on developing or applying advanced mathematical approaches to build computational frameworks capable
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, connect with international, European and national research programmes in biomimetics and related areas, and bring a space engineering perspective to these communities; build on and extend previous