The emergence of new imaging modalities, such as Near-Infrared and Photoacoustic Spectroscopy, has demonstrated the potential for wide applications in neuroscience, clinical neurology, and personal healthcare. However, processing medical images is conventionally done offline, due to restraints on computational capacity. These limitations hinder wider applications, particularly for ambient healthcare. However, Function as a Service (Faas) and serverless computing provide a tantalising alternative, utilizing lightweight virtual machine containers which widen the spectrum of applications that can be deployed in a cloud environment. This serverless architecture provides accessible distributed computational power outside of large cluster environments. The aim of this PhD project is to develop a new generation of serverless architecture for real-time medical imaging processing. This intelligent processing platform will be used for multi-modality imaging processing for diagnosis and patient healthcare. The PhD student will gain substantial knowledge in fields such as cloud computing, service-oriented architecture, AI hardware, optical imaging, and imaging processing, and will gain various machine learning and data analytical skills.
A first degree in a relevant scientific discipline, such as computer science, engineering, mathematics, physics, or medicine. Desirable skills include mathematics, statistics, machine learning, computer vision, and software engineering.
English Language requirement
IELTS score must be at least 6.0. Other equivalent qualifications will be accepted. Full details of the University’s policy are available online.
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