PhD – AI Hardware For Medical Imaging Processing


The emergence of new imaging modalities, such as Near-Infrared Spectroscopy (NIRS) and Photoacoustic (PA), have demonstrated the high potential for wide neuroscience, clinical neurology, and personal healthcare applications. However, the conventional way for data & imaging processing of NIRS and PA are offline processing and not in real time. Meanwhile, the processing unit is usually chosen as PC, which is bulky, heavy, and cumbersome, which further limit their wider applications particularly for personal at-home healthcare.

The aim for this PhD project is to develop new-generation AI hardware for on-board real-time medical imaging processing. Using FPGA and/or System on Chip with embedded AI algorithms such as machine learning, an AI hardware platform (that will be later integrated into multiple novel imaging technologies) will be implemented to miniaturize and localize the imaging processing units, to reduce the processing time, and to improve the processing quality, so as to realize on-board real-time signal processing and data fusion. This intelligent processing platform will not only be used for imaging processing, but also would be applied for Brain-Computer Interface, Human-Robot Interaction, and neuro-rehabilitation.

The PhD student will gain substantial knowledge in fields such as medical electronics & microelectronics, AI hardware, optical imaging, and imaging processing, and will learn various useful skills such as AI algorithms, hardware programming, microchip design, data processing, and will also gain significant practical experimental experience. These skills will be applicable in a wide range of future careers for the student.

The PhD student will join the dynamic and growing team at the HUB of Intelligent Neuro-engineering (HUB-IN) at School of Engineering and become part of collaboration between School of Computing Science and School of Physics & Astronomy at Glasgow.


For more information we would welcome questions to:


Tagged as: AI, BCI, Machine Learning, Neuroscience

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