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Digital communication plays a crucial role in enhancing aged care experiences, yet significant barriers exist, including accessibility, digital literacy, and cultural inclusivity. This project aims
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PhD Scholarship in ‘Using nanoparticles to enhance the immune response and improve vaccine efficacy’
sorting, multiplex cytokine analysis (Luminex), IVIS imaging, as well as ELISA, ELISPOT, immunohistology/immunofluorescence, proliferation and functional T cell assays. There is also potential scope to use
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and correlates will differ between younger adults and the elderly, and females compared to males. Methods: The laboratory uses world-class big-data ‘omics’ analysis of blood immune cells, including
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the cost of your tuition fees for the first four full-time equivalent years of your doctoral studies. As the scholarship recipient, you will have the opportunity to work with a team of leading researchers
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technologies, social structures, and networks. Effective C2 organisational systems are critical not only to military settings, but also to the operation of many civil domains, including emergency response
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the following skills and qualifications (tailored to the specific project): Driven individuals who want to be a part of a world class team Some familiarity in healthcare or engineering/image based analysis
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of up to AU$50,291 per annum. Start Date: July-August 2025 Additional financial support is available through research and teaching assistance work. The Research Opportunity This PhD research opportunity
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Candidate will use techniques such as germline/tumour sequencing data, plasma analysis and work with patients’ immune cells work to identify key functional targets. They may perform epigenetic techniques, DNA
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relevance across various surgical procedures and patient groups. Objectives 1. Algorithm Development: (a) Design and implement a deep learning algorithm for CT scan analysis. (b) Train, validate, and test the
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at the School of Electrical and Mechanical Engineering at the University of Adelaide and OptiGrid office at Lot Fourteen. This project will redefine machine-learning based electricity price forecasting by