288 phd-studenship-in-computer-vision-and-machine-learning PhD positions in Australia
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pioneering PhD project in partnership with ANCA, a world leader in CNC grinding machines and motion control systems. Supported by the prestigious Growth SUPRA PhD Scholarship, you'll join a dynamic research
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locations, fostering a rich and dynamic learning experience. Download the Program Guide for more information. Right across Australia, with 500 scholarships from Honours to PhD - your potential is limitless
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operation under reactive capture conditions. Unravelling reaction mechanisms and catalyst structure-performance relationships using advanced characterisation techniques and computational tools. Collaborating
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Applied mathematics, fluid mechanics, high-performance computer simulations. Full time, fixed term position (3 years) at Hawthorn campus $34,700 per annum (2025 rate) About the Scholarship Higher
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knowledge of health economics, but this is not a requirement as training will be available throughout the PhD program. Stipend: The scholarship will be for 3.5 years and has a stipend of $35,300 (indexed
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with digital twin modelling, remote sensing, and cloud computing is valued, as is a commitment to learning and advancing in these areas. The successful candidate will have the ability to work with large
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the youth residential care industry. This fully funded PhD scholarship offers the chance to work alongside leading researchers in residential care, systems thinking, and worker wellbeing at a world-class
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and hands-on experience with AI and computer vision. Solid programming skills in Python, especially with PyTorch. Practical experience with deep learning projects, including working with attention
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impact in translational fields, which often aim to use generative AI in a responsible way. This PhD project is part of a larger cohort of projects in the Monash AI Institute, a recently expanded program
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nanoparticles in drug delivery or diagnostics, familiarity with cellular targeting, and use of computational tools to model nanoparticle behaviour. Experience in a regulated research environment is also