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computer vision and machine learning methods to interpret the photovoltaic (PV) solar farm's condition and perform various inspections and anomaly detection. The research will draw from state-of-art
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publications and research experiences in structural dynamics and structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data
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-centric innovations, we aim to build a resilient agricultural and environmental future. Central to this effort is our AgriTwins program (a part of the CSIRO Next Generation Graduates Programs ), a
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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
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. • Proficient computer skills, including competence in the use of MS Office and other software packages, especially word processing, database and spreadsheet skills. About Swinburne University of Technology
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, Materials Science, or Manufacturing Engineering. Applicants should also ensure they meet the eligibility criteria for entry into a Swinburne PhD program. Full details can be found here . Additionally
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has become a world-class university, driving social and economic impacts through science, technology, and innovation. As a dual-sector university, our vision is for people and technology working
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chemoreceptor activity. Your work will bridge both computational and experimental research, including validation using in vivo models, optimising light delivery systems, and aligning in vitro and in vivo findings
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-reality application development; modern AI techniques (such as computer vision or large multimodal language models); and/or human-computer interaction. Our industry partners are developing software
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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