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Spectroscopy (MBI) conducts basic research in the field of nonlinear optics and ultrafast dynamics arising from the interaction of light with matter and pursues applications that emerge from this research. It
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quantify the impact of nonlinearity on flow and transportin confined porous media. We put particular emphasis on three sources of nonlinearity: non-Newtonian rheology, large driving rates leading to dynamic
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(ILM), interacting with PhD students, postdocs, and master's students on related topics. We seek a motivated candidate holding a master's in physics, with skills in ultrafast/nonlinear optics or soft
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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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the nonlinear dynamics of these networks using theory and numerical simulations. - Design and perform table-top robotic experiments that implement your learning algorithms in unpredictable environments. Who
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multiple techniques such as onboard safety monitoring, operating environment adaptation and real-time robust learning of uncertainties and nonlinearities within the dynamical system. In this PhD, the aim is
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and Ultrafast Optics group led by Prof. Dr. Jens Biegert. The group works in a highly interdisciplinary field which fuses ultrafast laser physics, extreme nonlinear optics, atomic and molecular
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◦ Controlled simulations (nonlinear SDEs, active models, dynamics with memory). ◦ Study of the robustness of the methods (noise, low sampling frequency, partial observability). 6. Application to real systems
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in the team of Boris Gutkin as the LNC2, ENS Paris. In the project the goal is to investigate how the nonlinear and compartmentalised neuron’s dendritic tree gives rise to recurrent network-like
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of compensating for nonlinear PA characteristics under dynamic operating conditions. Advanced machine learning and neural network approaches will be explored to improve linearization performance while reducing