100%, Zurich, fixed-term
The Chair in Nonlinear Dynamics at ETH Zürich is seeking a highly motivated PhD student in the area of data-driven model reduction for high-dimensional nonlinear physical problems. The successful candidate will work on the application and extension of data-driven nonlinear model reduction based on the recent theory of spectral submanifolds (SSM). SSM reduction is a dynamical-systems-based alternative to data-driven system modeling via neural networks, offering explicit and predictive polynomial ODE models for the observed dominant dynamics of nonlinear systems.
The objective of this position is to collaborate within a Swiss-French consortium of academic institutions to address complex model reduction challenges using SSM reduction. The successful candidate will compare this innovative approach with other model reduction methods, such as dynamic mode decomposition (DMD) and neural network-based techniques. Tackling these advanced problems will necessitate further mathematical and algorithmic developments beyond the current state of data-driven SSM reduction. The PhD position will be based either in the Department of Mechanical and Process Engineering or the Department of Mathematics at ETH Zurich, depending on the candidate's background and preferences.
ETH Zurich offers a collaborative and innovative working environment where you can contribute to meaningful research and education.
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Only applications matching the job profile will be considered.
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Location : Zürich
Country : Switzerland