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can occur that are very different to the macroscopic world. Our group develops methods to measure and ‘see’ this atomic detail using some of the world’s most powerful electron microscopes. We apply
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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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My interests span a wide range of topics in theoretical physics, including: geometric phases, topological defects in matter and radiation fields, inverse problems (scalar and vector tomography), singular optics, using electrons, atoms and light and the exploration of complex systems using...
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: finding planets, stars, and black holes through astrometric motion" "The fundamental physics that governs starlight" "First science with the Sloan Digital Sky Survey" "Data-driven methods for stellar
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Deepfakes, derived from "deep learning" and "fake," involve techniques that merge the face images of a target person with a video of a different source person. This process creates videos where the target person appears to be performing actions or speaking as the source person. In a broader...
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of mobile ringtones. Traditional machine learning methods and transformer models will be used to learn patterns from audio signals and classify ringtones into predefined categories (e.g., default ringtones
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Indigenous PhD Scholarship in Artistic research methods for music and science collaboration Job No.: 689424 Location: Clayton campus Employment Type: Full-time Duration: 3.5-year fixed-term
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PhD Scholarship - Novel methods to enhance the use of routinely collected linked data for the evaluation of government policies related to healthy ageing Job No.: 691415 Location: Frankston
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Program Coordinator Job No.: 690992 Location: Caulfield campus Employment Type: Full-time Duration: 2 year fixed-term appointment Remuneration: $83,424 - $95,825 pa HEW 5 plus 17% employer
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supervised by Dr Yi-Shan Tsai and A/Prof Roberto-Martinez Maldonado. They will work with the larger project team including Dr Sadia Nawaz, Prof Michael Phillips, Prof Danijela Gasevic, Prof Phillip Dawson, A