This project aims to develop a Python package to post-process 3-D hydrodynamics simulations (e.g., from RAMSES or Quokka) to create synthetic observations for JWST/HST/VLT.

A resolved star formation study of galaxies using TYPHOON

The student will upgrade an existing seismic classifier using modern machine learning techniques and expanded training sets.

You will use 3D magnetohydrodynamic simulations to model asymmetric accretion on protostars and their discs.

This project is to apply and develop software to integral field spectroscopic data of galaxies.

This project aims to determine what source is dominating in massive galaxies 4 Gyrs ago using MAGPI (MUSE), EMU (ASKAP) surveys and eROSITA public data.

The student will process a database of sink-particle produced from 4pc^3 star cluster forming simulations to study how young binary and multiple stars evolve.

This project aims to measure both distances and velocities for 100,000 galaxies and so map the visible and dark matter within a billion light-years.

In the spectra of distant stars, we can often find a series of absorption features, such as Diffuse Interstellar Bands (DIBs) or those of neutral atomic potassium, K I. Although the exact carriers of DIBs are still a matter of debate, various candidates have been proposed over the years.

Study of the statistics of turbulent, magnetised gases, relevant for the structure and evolution of the interstellar medium, the formation and evolution of stars and galaxies, using a combination of supercomputer simulations, theory, analytical calculations, and comparison to observations.

In each project, students will utilise data from some of the world’s most powerful radio telescopes, including ASKAP, Parkes, and the Jansky VLA to study the magnetised gas in and around the radio lobes inflated by supermassive black holes.

This project aims to study quenching in a unique rejuvenated/quenching galaxy found in deep Hubble/James Webb Space Telescope imaging data.

Dust in the Milky Way affects astronomical observations. Here we try too improve our knowledge of its distribution.

Are you ready to blend academic and commercial experience? Can you dive into details and also see the bigger picture? Are you interested in seeing how your knowledge can be applied to real-world outcomes? If so, we’d love to hear from you.

Do you enjoy working with instrumentation? Are you interested in using data to inform real-world decisions? Are you an independent thinker who loves to solve problems? If so, we’d love to hear from you.