This PhD project at Kingston University explores the cutting-edge field of neuromorphic computer vision, leveraging recent advances in bio-inspired neuromorphic hardware and sensors to develop efficient methodologies for real-time vision systems. The research aims to minimize cost, latency, and energy consumption in computer vision applications by processing event streams from neuromorphic cameras and applying neuromorphic machine learning techniques, particularly Spiking Neural Networks (SNNs). Key vision tasks addressed include object segmentation and recognition, human motion analysis, and video understanding. The project is situated within the Faculty of Engineering, Computing and the Environment, offering a dynamic research environment with access to state-of-the-art resources and expertise in software engineering and data analysis. Applicants should possess a first or upper second class honours degree or MSc in Computer Science, Engineering, Mathematics, Physics, or a closely related discipline. A strong programming background is essential, and candidates should be motivated to become experts in neuromorphic computer vision. The project is supervised by Professor Dimitrios Makris, an established researcher in the field, whose academic profile can be found here . Funding for this position is available through the Graduate School studentships competition for October 2026 entry. Details regarding funding, including tuition and stipend, can be found on the Kingston University PhD Studentships page. Prospective students are encouraged to contact Prof Makris at [email protected] for informal discussions about the project. The application deadline is March 4, 2026. To apply, review the studentships information and the Faculty research webpage, then submit your application via the university's official portal. The project is supported by a strong publication record in event-based vision and neuromorphic machine learning, as evidenced by recent conference and journal papers listed in the position description. This opportunity is ideal for candidates passionate about advancing computer vision through neuromorphic approaches and eager to contribute to innovative research in a collaborative academic setting.

This PhD project at Kingston University focuses on advancing Medical Image Analysis using Artificial Intelligence. The research aims to develop innovative computer vision and AI tools that automate the analysis of medical images from modalities such as MRI, CT, and ultrasonography. By integrating additional health-related data—including clinician reports, genomics, and longitudinal studies—the project seeks to enhance the accuracy and utility of AI systems in clinical decision support. Supervised by Professor Dimitrios Makris, an expert in computer vision and medical imaging, the successful candidate will join the Faculty of Engineering, Computing and the Environment. The research group is active in areas such as deep learning, radiomics, and software engineering, with recent publications addressing optimization of deep learning models for low-resource environments and efficient classification of glioblastoma sub-regions. Applicants should possess a first or upper second class honours degree, and preferably an MSc, in Computer Science, Engineering, Mathematics, Physics, or a closely related discipline. Strong programming skills and a keen interest in computer vision and medical image analysis are essential. The project is part of the Graduate School studentships competition for October 2026 entry, offering funding opportunities that may include tuition and stipend. For full details, candidates should consult the Kingston University PhD Studentships page and the Faculty research website. The application deadline is March 4, 2026. Prospective students are encouraged to contact Prof Dimitrios Makris ([email protected]) for informal discussions about the project. To apply, review the studentships information and follow the instructions provided on the university and faculty research pages. This opportunity is ideal for candidates seeking to become experts in medical image analysis, artificial intelligence, and computer vision, and who wish to contribute to impactful research supporting clinicians in healthcare decision-making.