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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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, natural sciences, economics, and mathematics education. Our goal is to create an environment that encourages innovative thinking and is responsive to current conditions and needs. This position, in
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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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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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, 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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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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solutions across the natural sciences. Your workplace You will be employed at the Department of Mathematics in the Division of Applied Mathematics, https://liu.se/en/organisation/liu/mai/tima . The research
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approaches, where nonlinear FEM, rule-based design, parametric design etc and general model-based systems engineering approaches are a plus. Experience applying artificial intelligence and machine learning
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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