Supervisor: Oliver Hahn (Department of Astrophysics, Department of Mathematics)

Funding Situation: supervisor has secured funding

Project outline: The nature of the dark components of our Universe, dark matter (including neutrinos) and dark energy, are certainly the biggest puzzles of contemporary physics, and finding observable signatures giving clues to their physical properties is one of the key research areas in present-day cosmology. The gravitational collapse of the tiny density fluctuations produced in the early Universe to the galaxies and large-scale structure we observe around us is directly impacted by the physics of the dark components. Turning this argument around, we can constrain the properties of neutrinos, dark matter and dark energy from observations if we have accurate predictions for the relation between them. Currently, the most precise predictions relating cosmological models and observable structures are based on cosmological simulations run on supercomputers.

In the context of this general research question, and taking into account interests and strengths, the potential candidate will focus on any one among the following topics:

1. using numerical simulations to understand better the small-scale properties of cosmic structures for different dark matter models and their observable implications, 
2. developing improved numerical techniques for large-scale cosmological simulations - in order make simulations more accurate and/or more economical, 
3. developing advanced data-driven statistical techniques (machine-learning) to model cosmic structure formation.