Outer space presents unique challenges for sensing and computing systems, including limited bandwidth, high communication latency, heat dissipation issues, cosmic ray exposure, and strict size and weight constraints due to launch costs. Despite these obstacles, space systems must process visual scenes with extreme dynamic ranges, low light, and high-velocity objects. This PhD project at The University of Manchester aims to investigate neuromorphic systems—especially event cameras—as innovative solutions to these problems. Neuromorphic systems offer high dynamic range, low latency, and low data rates, enabling event-driven computation that can outperform conventional cameras and GPUs in space environments.

The research will focus on the design, implementation, and evaluation of neuromorphic hardware and algorithms tailored to real-world space applications. The successful candidate will join the International Centre for Neuromorphic Systems (ICNS), a leading hub for brain-inspired hardware, algorithms, and applications. Access to state-of-the-art neuromorphic cameras, processors, and data from past space missions will be provided, along with a vibrant research community specializing in embedded systems, neuromorphic hardware, and space applications.

Eligibility: Applicants should hold (or expect to achieve) at least a 2.1 honours degree or a master’s (or international equivalent) in Computer Science, Engineering (Electrical, Mechatronics, or Robotics), Physics, or a related discipline. Strong programming skills (Python, Rust, C, C++), embedded systems experience, and excellent problem-solving abilities are required. Experience in machine learning, computer vision, or neuromorphic hardware is desirable but not essential. English language certification may be required for non-native speakers.

Funding: Excellent candidates will be nominated for competence-based faculty funding, which covers tuition fees and provides a tax-free stipend at the UKRI rate (£20,780 for 2025/26), with expected annual increases. Self-funded students are also welcome to apply. Flexible study arrangements, including part-time options, may be available depending on the project and funding.

Application Process: Applications are accepted year-round, but early application is recommended as the advert may be removed before the deadline. Contact the supervisors before applying and include details of your academic background, experience, and motivation. Apply online via the university website. Required documents include transcripts, CV, supporting statement, referee contact details, and English certificate (if applicable). Incomplete applications will not be considered.

Equality, Diversity, and Inclusion: The University of Manchester is committed to fostering a diverse and inclusive research community. Applications are encouraged from all backgrounds, including those returning from career breaks or seeking flexible study arrangements.

For further information, visit the FindAPhD project page or contact the admissions team at [email protected].