Sensorimotor integration in neuromorphic systems

About the Project

Humans navigate complex, dynamic environments every day throughout their lifespans. They recognise objects and other humans and can interact with the environment in a seemingly natural and trivial way. Behind all those behaviours is the human brain and its ability to naturally transform self-centred spatial information (e.g., egocentric) into environment-centred (allocentric) information and use it to achieve its objectives. Despite the vast literature on the visual system, there is no definitive model of how the brain performs those transformations.

This project aims to investigate egocentric-to-allocentric transformations from a computational perspective and to build spiking neural networks that learn these transformations. This project has two central pillars, one theoretical and one practical. To begin with, the theoretical part investigates the egocentric/allocentric visuomotor transformations on neuromorphic cameras and their integration with tactile information and motor control. This part will combine deep learning, mathematical modelling, and neuromorphic computing. Neuromorphic computing is a brain-inspired approach that designs hardware and software that mimics the way biological brains operate (neurons, synapses, and functions). Neuromorphic devices offer a power-efficient, embedded, and low-latency event-based platform. The second part will focus on developing an application that leverages neuromorphic systems, such as event-based cameras and event-based tactile sensors and will apply the theoretical results to problems including robotic navigation, brain-machine interfaces, and supernumerary robotic limbs. All the applications demonstrate the need for a highly robust, adaptive system to integrate visual and tactile information with motor control.

We are seeking a highly motivated and creative candidate with a strong academic background in Computational Neuroscience, Computer Science, Electrical Engineering, Physics, or a related discipline. The successful applicant will have a solid foundation in programming (e.g., Python, C/C++), machine learning, and deep learning. Prior experience in brain-machine interface and/or neuroscience is highly desirable, though not essential. The ideal applicant will possess excellent analytical and problem-solving skills.

The student will join the newly established International Centre for Neuromorphic Systems (ICNS), a world-leading hub for brain-inspired hardware, algorithms, and applications. They will have access to state-of-the-art neuromorphic devices, GPUs, and a vibrant community of researchers with expertise across embedded systems, neuromorphic hardware, and algorithms. The supervisory team offers expertise across neuromorphic computing, computational neuroscience, deep learning, and machine learning.

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

Furthermore, applicants should have a background in one of the following scientific areas: Computational Neuroscience, Computer Science, Electrical Engineering, Physics, or a related discipline; experience in programming (e.g., Python, C/C++); and experience in machine learning and deep learning.

Funding

Excellent candidates will be nominated for competence-based faculty funding. The funding covers tuition fees and provides a tax-free stipend based on the UKRI rate (£20,780 for 2025/26). We expect the stipend to increase each year. The start date is October 2026.

For more information, and funding deadlines please visit our FSE Funding home page. Your supervisor will be able to advise you on specific scholarships, studentships and awards you may be eligible for.

Self-funded students are welcome to apply.

We recommend that you apply early as the advert may be removed before the deadline.

For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for.

Before you apply

We strongly recommend that you contact the supervisors for this project before you apply. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

How to apply

Apply online through our website: https://uom.link/pgr-apply-2425

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language certificate (if applicable)

If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.