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This scholarship is jointly funded by a leading computer vision company in Australia and the STEM College of RMIT University. The research includes investigation into automated product quality
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process from biomass. In the PhD program, you will be involved in working with a multi-disciplinary team. You will be conducting experiments and developing phenomenological models to understand the graphite
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. This will deliver an accurate model of inductively heated polymer melt-flow processing in larger scale systems. A short horizon to very significant impact is likely. An expanded understanding of these very
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stiffness by changing their composition and self-assembly process. The relationship between nanoparticle structure and stiffness will be determined both through experimental and modelling approaches. Finally
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PhD Scholarship in ‘Using nanoparticles to enhance the immune response and improve vaccine efficacy’
are interested in nanoparticles of different materials and compositions to compare to our standard biocompatible and non-inflammatory polystyrene nanoparticles in animal vaccine models, as well as their mechanism
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investigations at macro, micro and molecular levels. Modeling tools such as HSC Chemistry and ASPEN Plus will be used to identify most suitable low cost catalysts/minerals and to perform process modeling
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to investigate means of recycling waste heat from sewer systems in the food processing sector. Are you passionate about energy efficiency and developing technology to reduce climate emissions? We are offering a
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, applied maths, physics, chemistry, computer science, computational modelling, earth science or similar. Strong programming skills. Experience in making gas flux measurements Excellent project management
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PhD Scholarship in Integrated Photonics for Telecommunication, Biosensing and Precision Measurements
Sensor surface biofunctionalisation Optical communications High-speed signal analysis Modelling of optical propagation in waveguides and fibres Digital signal processing Design and analysis of photonic
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machine learning approaches show issues in model performance and efficiency and vulnerability towards the application of noise over a large number of distributed models. These issues should be overcome by