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aerospace and space systems while remaining computationally tractable. A key objective is to transform theoretical advances into practical tools that can support analysis, design and decision-making in a
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terrain, the co-evolution of morphology and control for soft and legged robots using differentiable and evolutionary optimisation, and gravity-aware locomotion strategies for mass- and energy-limited
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(documented by publications and grant applications); Demonstrated knowledge of the relevant regulatory frameworks, including, but not limited to the eIDAS (2.0), the DSA, and the DMA. Demonstrated experience in
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feedback. Classroom implementation and evaluation. Support and co-lead pilot studies in real CBL classrooms; contribute to data collection, analysis, and interpretation of the agent's performance, attending
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learning for the analysis of population-scale networks. The position focuses on two pillars of trustworthiness: explainability and privacy-preserving learning. This research direction connects closely