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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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explore unconventional ideas, develop computer algorithms for data analysis, create new experimental approaches, and apply the technique in areas like biomedicine, materials science, and geology. My group
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I study galaxies and active galactic nuclei, endeavouring to understand how galaxies grow, what regulates star formation within galaxies and what triggers active galactic nuclei. My students and I use imaging surveys at X-ray, optical, infrared and radio wavelengths to measure the emission from...
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faced by real people and creating a lasting impact. Join us and help to create a world that is more inclusive and equitable. If you are interested in undertaking a PhD with MATS and you meet the criteria
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" "Machine-learning-based imaging processing" webpage For further details or alternative opportunities, please contact: haoran.ren@monash.edu.
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I am an experimental particle physicist and I specialise in the study of particles containing the beauty and charm quarks. My research aims to help improve our understanding our universe by comparing our experimental observations to predictions made using the Standard Model of Particle...
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spectroscopy and Gaia data of star clusters to decipher the mystery of the Lithium-rich giant stars" (with Prof John Lattanzio) "The origin of the heavy elements: Computer simulations of neutron-capture
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of Excellence for Future Low-Energy Electronics Technologies. I have projects available within the following areas, all of which can be tailored to either honours or PhD level. "Quantum impurities in quantum
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The goal of my research is to synthesize and characterize low-dimensional nanomaterials with atomic-scale precision and tailored electronic, optoelectronic, magnetic and chemical properties. In my group, we synthesise these functional nanomaterials from the bottom-up, using protocols of...
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they bond in materials, but also develop transferable skills in scientific computing, data analysis and visualisation. "Machine learning for atomic-scale structure determination in thick nanostructures" (with