258 phd-studenship-in-computer-vision-and-machine-learning PhD positions in Australia
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I am interested in all aspects of theoretical astrophysics, with a particular focus on strong gravitational fields, compact objects, and gravitational-wave astronomy. I am currently exploring the evolution of massive binary stars into compact binaries as sources of gravitational-waves and...
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I work on a wide range of topics with my local group and in collaboration with members of three large international collaborations. The central focus of my research is to understand how the observed pattern of fundamental particles and forces emerged, using information carried by gravitational...
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I supervise a wide range of projects at the intersection of photonics and nanotechnology, investigating how we can efficiently control light on the nanoscale. Applications are in areas such as optoelectronics, green energy, and fundamental quantum optics. As a member of my group you will have...
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HDR Scholarship in flow assurance and corrosion mitigation at Curtin Corrosion Centre. Curtin University, in collaboration with Chevron Australia, offers one fully funded PhD positions. This research
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Scholarship code: SRS-25035 This inaugural Industry PhD scholarship with the Department of Health and La Trobe University will be awarded to an outstanding Maternal and Child Health (MCH) nurse
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group of PhD researchers who will tackle the most pressing questions in Machine Learning while ensuring AI serves humanity responsibly. You'll work within one of our specialised research themes, each
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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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computers to large-scale multi-dimensional simulations on high-end supercomputers, depending on your interests and inclinations. "Modelling extreme supernova explosions: From fast and faint to bright and
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challenging for clinicians and pregnant women. Digital health records, advances in big data, machine learning and artificial intelligence methodologies, and novel data visualisation capabilities have opened up
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analytical imaging methods, then working with collaborators to apply these methods to biomedical research, diagnostic imaging and beyond. Research projects vary from purely theoretical, to computational