Anthony Arnold

    Research interests: tree and direct N-body simulations, black holes and gravitational


   Sanjay Bloor

    Research interests: the particle nature of dark matter, complementarity of
    experimental signatures in physics beyond the Standard Model, computational
    tools in particle physics


   Zac Byrne

    Research interests: extragalactic astrophysics, focussing on element abundances
    and their effect on the formation and life of galaxies. Also galaxy simulations and
    black holes.


   Joshua Calcino

    Research interests: supernova cosmology with the OzDES galaxy survey.


   Anthony Carr

    Research interests: supernova Cosmology, redshift accuracy, systematic errors
    and their effects on cosmological parameters


   Chris Chang

    Research interests: Super-Symmetric Extensions to the Standard Model and
    the appearance of acausality in physics.


   Simon Deeley

    Research interests: galaxy evolution and its dependence on the surrounding
    environment, currently focusing on how spiral galaxies transform into
    lenticular galaxies.


   Aaron Glanville

    Research interests: exploring the effects of redshift errors on cosmological
    constraints, BAO cosmology and the General relativistic description of
    expanding space


   Ellie Leitinger

    Research interests: metallicities and kinematics of globular clusters,
    spectroscopy and photometry, active galactic nuclei and reverberation
    mapping of black holes


   Rebecca Jane Mayes

    Research interests: using cosmological simulations to study galaxy
    disruption in large galaxy clusters, with the goal of predicting the number of
    supermassive black holes that are located within ultra-compact dwarf galaxies.


   Andrew Penton

    Research interests: Supermassive Black Hole mass measurements using
    reverberation mapping, Active Galactic Nuclei (AGN) and general relativistic
    Black Hole simulations.


   Gary Segal

    Research interests: information-theoretic perspectives on the second law of
    thermodynamics.The application of machine learning in big data radio
    astronomy, in particular, measures of 'interestingness' and unsupervised learning
                                 approaches for detecting the unexpected.