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cover letter briefly outlining your interest in the project Evidence of research ability, such as a digital copy of a Masters or Honours thesis A digital copy of academic transcripts A CV including any
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conditions have been satisfied. Project description/summary: This Project will develop a model of material dynamics and heat transfer within a commercial vertical induction heating reactor. This novel reactor
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grow new materials by molecular beam epitaxy, or create heterostructures of materials by exfoliating and stacking atomically thin layers from crystals, and fabricate nanoscale electronic devices
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PhD Scholarship in ‘Using nanoparticles to enhance the immune response and improve vaccine efficacy’
Plenbanski via magdalena.plebanski@rmit.edu.au and Dr Kirsty Wilson via kirsty.wilson2@rmit.edu.au . A copy of electronic academic transcripts A CV that includes any publications/awards and the contact
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will work within a dynamic, multidisciplinary research environment, with access to state-of-the-art facilities and direct industry engagement. About You To be suitable for this role you will need to have
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technological solutions for the recycling of waste heat in specific food industry settings, using computational fluid dynamics modelling, lab experiments and field work To disseminate finding in high impact
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, especially in ultracold quantum gases or condensed matter theory Proven analytical, computational, and modelling skills Experience with numerical simulations of quantum or many-body systems A deep curiosity
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PhD Scholarship in Integrated Photonics for Telecommunication, Biosensing and Precision Measurements
will be trained is some of these different skills: Micro/nanofabrication Simulation/design of optical components Integrated photonic circuits Programming skills in Python, C/C++ Theory of electromagnetic
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These models are technology platforms that facilitate drug discovery and fundamental research. Our research is funded by Australian Research Council. This project aims to develop bio-microfluidic
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predictive capability for post-TAVR outcomes against traditional metrics. iii) Incorporate the AI-based frailty evaluation into surgical risk scores for a comprehensive risk prediction. iv) Examine the model's