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Human interventions and climate change are exerting increasing pressure on estuarine systems, profoundly altering their dynamics and often leading to negative consequences such as reduced flood
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challenging at temperatures approaching 20 K. Develop new methods to reveal how aerospace materials deform and become damaged under cryogenic and dynamic loading. Your research will contribute to safer hydrogen
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theoretical and computational research on dynamic game theory and multi-agent optimization for uncertain systems. By building on tools from distributed optimization, convex-monotone game theory, and hybrid
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dynamics (MD) simulations capable of probing hydrogen diffusion and trapping at interfaces in the presence of tramp elements with near-DFT accuracy Collaborate closely with a broad team of researchers from
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part of a new lab at the Delft Center for Systems and Control, supervised by Gabriel de Albuquerque Gleizer. The research will allow you to gain deep insights across nonlinear dynamics, optimization, and
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) for a multi-component Fe alloy system, enabling predictive molecular dynamics (MD) simulations capable of probing hydrogen diffusion and trapping at interfaces in the presence of tramp elements with near
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of dynamic radar scenes. The project combines methodological machine learning research with experiments on real automotive sensor data. You will have access to research vehicles and advanced radar prototypes
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also include generative or predictive modeling of dynamic radar scenes. The project combines methodological machine learning research with experiments on real automotive sensor data. You will have access
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controllers for complex systems that are safe and verifiable by design. Information Neural networks can provide the flexibility needed to control increasingly complex dynamical systems, but their opaque and
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by a more experienced theoretical postdoc. In the second phase of the project, work will move towards current-driven dynamics of the nanosized textures, the exploration of topological excitations, and