Supervisor: Dr Gregorio Carullo (Email g.carullo[at]bham.ac.uk)

Coalescences of two black holes are the most powerful cosmic events happening throughout the Universe. Their dynamics sources the strongest gravitational fields experimentally accessible, and gives rise to violent space-time deformations known as gravitational waves. The observation of gravitational waves by the large scale LIGO-Virgo interferometers on Earth has opened a golden era of strong-gravity exploration, powering the largest surge ever witnessed in gravity-related research worldwide. Despite rapid progress, many open questions still remain, as our understanding of the general-relativistic two-body problem is far from complete.

The PhD candidate will delve into strong-gravity research, and gain experience into both advanced modelling methods in General Relativity and cutting-edge statistical inference techniques deployed on large supercomputers, applied to gravitational wave signals detected by current earth-based observatories or upcoming space-based missions. Details of the project will be agreed with the candidate depending on their interests and inclinations, but specific examples of challenging problems on which the aforementioned skills could be used include: measuring yet unobserved black holes vibrational spectra, modelling the near-merger two-body dynamics, developing innovative inference techniques targeting highly eccentric binaries, or probing the nature of black hole event horizons.

Supervisor: Dr Geraint Pratten (Email gpratten[at]star.sr.bham.ac.uk)

Compact binaries, consisting of pairs of black holes or neutron stars, are extremely powerful sources of gravitational radiation. Vital information on the physics that drives the coalescence of these binaries is directly encoded in the gravitational-wave signal. By decoding this information, we can obtain crucial knowledge on the origin and evolution of astrophysical black holes and neutron stars throughout the Universe. Our ability to extract this information requires developing new and increasingly sophisticated theoretical models for the gravitational-wave signal. This PhD project will aim to tackle key questions in gravitational-wave astronomy and fundamental physics. Research topics include: the analytical modelling of gravitational dynamics and radiation, numerical relativity simulations of compact binaries, acceleration techniques for gravitational-wave data analysis, testing fundamental physics in the strong-field regime, modelling neutron star mergers, and exploring our understanding of the neutron star equation of state.

The PhD Student will be strongly encouraged to join the LIGO Scientific Collaboration and be given the opportunity to work directly with gravitational-wave data and to play a role in the gravitational-wave discoveries made by the LIGO detector

Supervisor:Supervisor: Dr Patricia Schmidt (Email: P.Schmidt[at]@bham.ac.uk)

Gravitational waves from colliding neutron stars provide a unique opportunity for probing the properties of ultra-dense matter and fundamental interactions in extreme regimes. This PhD project focuses on modelling tidal effects in the waveforms emitted by binary neutron stars, and subsequently applying these models to parameter estimation and population inference, with an emphasis on determining the as-of-yet unknown neutron star equation of state (EOS).

The successful candidate will work to create cutting-edge models using post-Newtonian (PN) and numerical relativity techniques to better capture the complex physics of neutron star systems. They will then apply these models within a Bayesian inference framework to analyse gravitational-wave data, enabling new insights into the structure of neutron stars and EOS properties.

This project offers a unique opportunity to innovate in waveform modelling and conduct novel analyses of gravitational-wave observations. They will also be encouraged to join the LIGO Scientific Collaboration. Ideal applicants will have a strong background in theoretical astrophysics and general relativity, computational and analytical skills as well as core statistics knowledge.