Activities
About the CRC
The Collaborative Research Centre “Port-Hamiltonian Systems” (CRC 1701) provides a solid mathematical theory for the analysis, discretization and optimization of port-Hamiltonian systems. This provides a deep understanding and, in the long term, improvements to the methods that are already being used successfully in engineering.
Call for applications (deadline: July 31, 2025)
Short term fellowships for Master and PhD students
Short term fellowships allow motivated Master or PhD students to establish a close contact and to work with the CRC for a shorter period of time.
Call for application (deadline: July 23, 2025)
Postdoc position in the project “Multi-coupled port-Hamiltonian models for electromagnetic problems"
Call for application (deadline: July 15, 2025)
PhD position "Data-driven surrogate modelling for differential-algebraic port-Hamiltonian systems"
Funded by the German Research Foundation, the Collaborative Research Center CRC 1701 “Port-Hamiltonian Systems”, invites applications for the project “Data-driven surrogate modelling for differential-algebraic port-Hamiltonian systems” of the CRC 1701.
The position covers structure preserving scientific machine learning for port-Hamiltonian ordinary differential equations and differential algebraic equations.
Port-Hamiltonian systems represent an important and attractive new paradigm for the mathematical modeling of coupled dynamical systems. Through a systematic formulation of the ports (inputs), several systems can be coupled or large systems can be broken down into subsystems without losing their central properties.
The position focuses on learning the dynamics of port-Hamiltonian ordinary differential equations and differential algebraic equations. Gaussian processes can be used herein as surrogate models that make it possible to treat nonlinear Hamiltonian or effort functions in port Hamiltonian differential equations, even and especially if they are not explicitly known. The particular work will be on how such surrogate models can be constructed from measured and synthetic data, in an efficient way, while preserving the special structure of the underlying system, hence it is a special stricter kind of being physics informed.
Job description (German and English)
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