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dynamical structure directly from time-series data. This includes methodological work on nonlinear state-space reconstruction, system identification, reservoir computing and related recurrent architectures
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, when dynamics are complex, nonlinear and partially unknown, such a model is typically obtained from observations by performing system identification -- one notable example is given by Gaussian process
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systems. However, when dynamics are complex, nonlinear and partially unknown, such a model is typically obtained from observations by performing system identification -- one notable example is given by
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systems. However, when dynamics are complex, nonlinear and partially unknown, such a model is typically obtained from observations by performing system identification. Typical identification algorithms
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of dynamical systems. However, when dynamics are complex, nonlinear and partially unknown, such a model is typically obtained from observations by performing system identification. Typical identification
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aims to develop hybrid quantum–classical approaches for modeling multiphase flows governed by complex, nonlinear dynamics across multiple scales. The postdoctoral researcher will investigate how
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numerical analysis, computational fluid dynamics, and uncertainty quantification with diverse applications. Our group maintains active collaborations with other divisions at Linköping University and broader
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with provable performance for nonlinear systems. About us The Department of Mathematical Science provides a creative, dynamic and innovative environment where research, education, and societal
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-network computing. The work includes modeling coupled nonlinear dynamical systems, developing learning and inference schemes for neuromorphic/reservoir and Ising-type computation, and benchmarking
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) for exploration and inspection Soft and morphing aerial robots with pneumatic actuation Navigation in GPS-denied, cluttered, or dynamic environments Advanced Control and System Integration Nonlinear, adaptive, and