Dr. Chamandy is a postdoctoral fellow at the University of Rochester. His research: Magnetic fields exist almost everywhere in the universe, and play a crucial role in many astrophysical processes. The evolution of magnetic fields within galaxies over cosmic time is governed by magnetohydrodynamics. Dr. Chamandy and his fellow researchers are in the process of developing a hybrid scheme, wherein output from a semi-analytic galaxy evolution model (GALFORM), which in turn uses output from a cosmological dark matter simulation (MILLENIUM), is used as input for galactic dynamo mean-field simulations. These simulations solve a suitably simplified set of MHD equations to compute the magnetic field for each galaxy. Thus we hope to simulate the temporal and spatial evolution of the magnetic fields of >10^7 galaxies over cosmological timescales and volumes. The goal is to produce mock MeerKAT/SKA radio polarization data sets and images, which can then be compared with observations.
Dr. Randriamanakoto is a SKA research fellow at the University of Cape Town.
Her research interests include deep radio continuum surveys to probe faint polarized radio sources and to understand star formation and galaxy evolution in general. The project is part of the science commissioning phase of the large MeerKAT MIGHTEE survey.
Angus Comrie is a visualisation developer at IDIA. He holds a PhD in nuclear physics from UCT and is currently developing systems to visualise astronomical data for SKA precursor large projects.
Jon holds a SKA research fellowship jointly at UWC and UCT. He completed his PhD in observational cosmology through radio astronomy in 2007, in particular using radio-continuum studies of the Cosmic Microwave Background to search for clusters of galaxies using the so-called Sunyaev-Zel'dovich effect.
He specializes in statistical (especially Bayesian) analyses of interferometric data, whether in radio maps or directly in the 'visibility' domain in which they are taken. He models the noise properties of the data, in order to extract the maximum amount of information possible.