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an ARC Linkage Project focused on developing an autonomous system for detecting and quantifying structural damage in infrastructures (e.g., bridges, grain silos) using computer vision, digital twins, and
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that electrodes strategically fouled with non-conductive plastics are better performers than perfectly clean electrodes. Electrolysis rates of organic molecules are augmented where electrode, hydrophobic insulator
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group, which provides a collaborative environment with many PhD students working on cutting-edge research. The project will have access to high performance computational resources, including a server
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to three years of study (with the potential for extension) Scholarship Details Maximum number awarded 1 Eligible courses The successful candidate must enrol into a Doctor of Philosophy (PhD) program
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include: the evaluation of an existing health prevention program, the development of a measurement tool for health inequalities, behavioural experiments to assess how preventative interventions can improve
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numerical modelling framework to simulate how light and heat interact with the target body tissue, while also incorporating neural signalling dynamics to explore how light-based stimulation affects
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addition to debits, credits can be obtained. The research performed under this internship by an Electrical/Power Engineering or Computer Science PhD student will relate to this reform, specifically investigation
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Looking For: We’re looking for a driven and capable student who is eager to tackle complex, high-impact problems in quantum physics. The ideal candidate will have: A strong background in theoretical physics
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was initiated for the management of asymptomatic bacteriuria in RACHs, which is deemed inappropriate. High antimicrobial use for suspected UTIs (29.8%) was also recently reported in the 2020 Aged Care National
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Training Program (RTP) Stipend www.monash.edu/study/fees-scholarships/scholarships/find-a-scholarship/research-training-program-scholarship#scholarship-details Be inspired, every day Drive your own learning