Home

News: PhD position on black holes at University of Hertfordshire

Applications for PhD positions are open to work with me at the University of Hertfordshire on demographic studies of black holes across cosmic time. Further details are given below:

Supermassive black holes (SMBHs) residing at the centres of most galaxies play a pivotal role in regulating star formation within their hosts. This process, known as Active Galactic Nucleus (AGN) feedback, operates via powerful winds and outflows launched from the black hole’s accretion disk and/or collimated jets observed in the radio. These winds can either expel the cold molecular gas that fuels star formation or heat it sufficiently to prevent stars from forming. AGN feedback has become a key ingredient in several cosmological hydrodynamical simulations that model galaxy formation and evolution.
Despite decades of observations of galaxies hosting AGN, the physical details of this feedback process remain poorly understood. We are still far from establishing a comprehensive picture of how black holes influence their host galaxies across the broader galaxy population. This is particularly true for galaxies hosting low-mass black holes or black holes with low mass accretion rates, which are thought to have been the dominant population throughout much of cosmic history.
In the 2020s and 2030s, large-scale statistical spectroscopic surveys of AGN host galaxies will provide unprecedented opportunities to explore these questions. This PhD project will utilise state-of-the-art spectroscopic data from the 4MOST CHANGES community survey (Bauer et al. 2023). 4MOST is the largest spectroscopic facility in the Southern Hemisphere and you will become part of a diverse international collaboration working to detect fast accretion-disk winds and to characterise the interstellar medium of AGN host galaxies (stellar age, metallicity) and their connection to black hole mass and luminosity. You will also integrate these observations with data from publicly available spectroscopic surveys to build a more complete statistical framework.
Beyond its scientific goals, this project also offers extensive opportunities to develop interdisciplinary expertise in data science, data management, and machine learning, given the statistical nature of the datasets.

Details on how to apply are available here