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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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Graphs/ AI methods to this domain. This position has a two-stage selection process: Stage 1: Please submit an EOI using the Faculty of Information Technology Research Project Enquiry form here
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conversion of biomass into high-value platform chemicals and bio-slurry fuels. The project builds on our patented and published bench-scale work, aiming to scale up the process to continuous operation using
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for probing the atomic world. Co- supervisors are typically collaborators from within the Physics of Imaging group. Example project areas are: Developing ways to image atoms in space, energy and time Designing
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Conventional x-ray imaging is firmly established as an invaluable tool in medicine, security, research and manufacturing. However, conventional methods extract only a fraction of the sample
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supergiant stars right before the explosion Searching different astrophysical channels that produce r-process elements Connecting the properties of long-duration gamma-ray bursts and associated supernovae web
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use imaging surveys at X-ray, optical, infrared and radio wavelengths to measure the emission from stars, active galactic nuclei, warm dust, atomic hydrogen and relativistic electrons. Spectroscopic
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will design quantum-safe threshold encryption and/or authentication algorithms. The expected outcome is the design of methods, techniques and their software prototype to implement quantum-safe threshold
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I supervise computational projects in electron microscopy imaging for investigating materials at atomic resolution. Some projects centre on analysing experimental data acquired by experimental
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that are critical of generative artificial intelligence will not be disadvantaged in the selection process, but must still meet all other requirements. The below themes are offered as inspiration for research