For BSc and MSc theses the following projects are available:
  • Bayesian/maximum likelihood and machine learning data analyses: use objective analysis methods to identify the underlying physical process encoded in measured or simulated data. In disordered systems typically the laws of diffusion are no longer universally governed by the Gaussian solution for Brownian motion. It is then often intricate to tell from recorded data which precise physical laws are followed in the observed system. Methods such as Bayesian/maximum likelihood and machine learning methods, in combination with our advanced physical observables, offer elegant ways to extract the physics from the data.
    Compare, e.g., Phys. Chem. Chem. Phys. 20, 29018 (2018); J. Chem. Phys. 150, 144901 (2019)
  • Hetergogeneous and correlated diffusion processes: use analytical and numerical tools to study normal and anomalous diffusion processes in inhomogeneous environments or in the presence of correlations. These are standard models for diffusion in complex systems such as biological cells. New modesl are needed to accommodate relevant situations, e.g., boundary conditions.
    Compare, e.g., New J. Phys. 21, 022002 (2019) ; New J. Phys. 15, 083039 (2013)
  • Simulations of crowded systems: use Langevin dynamics simulations to model physical processes in systems, that are crowded with finite-sized particles. The cytoplasm of biological cells is crowded with a plethora of macromolecules and other objects. This significantly changes the physical laws of diffusion and active motion. Alternatively, the environment may be structured. e.g., in hydrogels, another direction of potential research.
    Compare, e.g., New J. Phys. 18, 013027 (2016) ; New J. Phys. 17, 113008 (2015) ; New J. Phys. 16, 092002 (2014);
  • Active motion in heterogeneous systems: Generalise existing theories for diffusion in heterogeneous environments to processes of active motion. Motile cells, e.g., amoeba, actively move on surfaces. Their transport laws often deviate from simple models. Use simulations and analytical methods to explore different motion patterns.
    See, e.g., Phys. Chem. Chem. Phys. 20, 23034 (2018); Phys. Rev. X 7, 021002 (2017)
  • Quorum sensing mechanisms: use analitical approaches and simulations to study how cells such as specific bacteria transition from individual, motile behaviour to form biofilms. Special interest is on the disordered nature of early biofilm structures and the associated crossover dynamics from individual to social behaviour.
    See, e.g., Sci. Rep. 9, 12077 (2019)
  • Chemical reactions and gene regulation: proteins and other signalling molecules diffuse to their designated reaction site to bind and prompt chemical effects. For instance, a repressor protein causes the shutdown of a specific gene. Use analytical methods and simulations to explore such mechanisms for different scenarios including gene regulation in cells with a nucleus.
    Compare, e.g., Phys. Rev. Lett. 110, 198101 (2013) ; Comm. Chem. 1, 96 (2018) ; Phys. Rev. X 6, 041037 (2016)
Further projects may be agreed upon, feel free to contact us. Check our publications whether you find a topic that interests you.

PhD or postdoc projects: if you are interested to pursue a PhD or postdoc project in our group, please contact Prof Metzler ( Apart from positions funded directly by the group, there is also the possibility of PhD/postdoc fellowships. We have had several successful projects funded by Alexander von Humboldt foundation or German Academic Exchange Service (see their various national pages). For postdocs a relatively new option is the Walter Benjamin programme by German Science Foundation (DFG).