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Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling
Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling Job No.: 683222 Location: Clayton campus Employment Type: Full-time Duration: 3.5 to 4-year fixed
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PhD Scholarship – Modelling the social and political drivers of net zero transitions Job No.: 670767 Location: Clayton campus Employment Type: Full-time Duration: 3.5-year fixed-term appointment
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I am interested in the most catastrophic and explosive collisions in the Universe, such as the mergers of neutron stars and black holes. I study these using both gravitational waves and
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pattern of fundamental particles and forces emerged, using information carried by gravitational waves from the earliest moments of the Universe. To this end, I collaborate with the Global And Modular BSM
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stellar interiors, birth properties of black holes and neutron stars, supernova light curves and spectra, gravitational waves, neutrino astrophysics, the production of heavy elements stellar explosions, and
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with the Australian Square Kilometre Array Pathfinder The spectral energy distributions of galaxies web page For further details or alternative project arrangements, please contact: michael.brown
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My primary areas of research activity are two fold: first, studing thermonuclear (X-ray) bursts from accreting neutron stars; and second, searches for optical counterparts of gravitational-wave
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-field imaging of dynamic processes" "Multi-scale X-ray speckle-based imaging" "Spectral X-ray speckle-based imaging" "Single-shot multi-projection X-ray phase-contrast imaging" "X-ray virtual histology
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I supervise a wide range of projects in gravitational-wave astronomy. This work is carried out within the Centre of Excellence for Gravitational-wave Discovery: OzGrav. As a member of my team, you
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, spectroscopy, astrometry) using massive optical telescopes on Earth and in space (e.g., Hubble, Gaia, JWST, Kepler, TESS). My group develops cutting-edge models to extract the most from noisy data and to better