64 phd-studenship-in-computer-vision-and-machine-learning PhD positions at Monash University
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aims to explore who takes physics and astrophysics major units, why they pursue them, and what obstacles they may face. There are a number of research questions under this umbrella. Computational
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My research explores ways to determine the atomic architecture of materials so we can understand and manipulate a material’s behaviour. At the atomic level, amazing and beautiful quantum phenomena can occur that are very different to the macroscopic world. Our group develops methods to measure...
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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 supervise a wide range of PhD projects on experimental research into the electronic properties of novel quantum materials including topological insulators, graphene, and other atomically thin two
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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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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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world, and with world-class photonic facilities at Monash. "Quantum nanophotonic chip" "Multimode imaging through ultrathin meta-optics" "Advancing optical imaging with flat optics" "Machine-learning
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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