• Algorithmic approximation of the fibre directions in the ventricles of a healthy heart.
  • Electrophysiological simulation of the sinus rhythm in the ventricles. Plane waves in the membrane potential are followed by contractions of the heart muscle, which ensure proper pumping of the blood into the pulmonary system and the body.
  • Electrophysiological simulation of ventricular fibrillation (Kammerflimmern). Spiral waves in the membrane potential prevent the full contraction of the heart muscle, which hinders proper pumping of the blood into the pulmonary system and the body.

Welcome at the website of the CP4LS research group

The CP4LS group Computational Physics for Life Science is fascinated by the complex processes and phenomena in the field of biological physics. We are driven by the urge to understand governing mechanisms of these processes with the aim to improve existing and develop novel medical applications.

In particular in the field of medical applications, numerical simulations constitute a powerful and essential tool for testing new hypothesis, provide new insights into fundamental mechanisms and are often able to reveal and assess (potentially harmful) risks of applications. These objectives can be addressed in a rigorous manner without exposing patients to considerable risks.

Our research projects aim to address the following specific


  • How can we exploit latest developments of measurement and imaging techniques for a detailed characterization of the heart?
  • What are the governing mechanisms for the onset and perpetuation of cardiac arrhythmia and how can they be controlled in an efficient way?
  • How can machine learning algorithms contribute to improve medical applications, e.g. for the development of efficient control strategies for cardiac arrhythmia?

To achieve our goal to promote the development of novel applications in life science, we combine innovative simulation frameworks with state of the art medical measurement techniques and latest machine learning algorithms.



Currently, we are interested in characterizing the spatio-temporal dynamics underlying cardiac arrhythmia by analyzing data from state of the art measurement techniques and developing novel efficient control approaches. Among others, our research focuses on the following topics:

  • simulations of electrical excitation waves during cardiac arrhythmia
  • classifying electrical wave patterns during ventricular fibrillation by deep neural networks
  • developing control strategies for chaotic wave patterns underlying cardiac arrhythmia


Diolosa, Laura Bachelor student  
Lilienkamp, Thomas Head of research group  
Suth, Daniel Scientific assistant  
Wagner, Elina Bachelor student  

Open positions

We are constantly seeking for enthusiastic students who are interested in addressing relevant open questions in the field of life science, biological physics and medical applications with (open source) numerical methods and machine learning algorithms.

We can offer topics for

  • Student helpers (Studentische Hilfskraft)
  • Projects (Anwendungsprojekte)
  • Bachelor thesis
  • Master thesis

For more information, please do not hesitate to ask us.