About the Project
This PhD project is built on a multidisciplinary collaborative project between Anglia Ruskin University (AI) and University of Cambridge (Electronic Engineering Division in the Department of Engineering). This project will investigate the convergence of Edge AI and Neuromorphic Computing to enable adaptive, low-power robotic systems.
Supervision Team:
Background and Motivation:
As robots become increasingly integral in society, they must adapt to changing environments and cooperate with human partners effectively. Traditional AI systems, such as neural networks and deep learning algorithms, have excelled in tasks like computer vision, image recognition and large language models (LLM). However, their reliance on extensive computational resources results in excessively high energy consumption, making them unsuitable for energy-constrained applications such as edge devices and social robots. If the energy issue is not addressed, it will become a major constraining factor for the continued advancement of AI.
Neuromorphic computing offers a promising approach for innovation in this space. Neuromorphic systems, processing information using spiking neurons and synapses, enable energy-efficient, brain-like decision-making capabilities. This project will aim to develop effective approach to enable the integration of neuromorphic electronics/computing and edge AI to support low-power robotic systems that sense, think, and act efficiently in real-time and real-world environment. In particular, the project will investigate the opportunity for embedding AI processing around a robotic system. For example, a gripper coated with a flexible ‘smart skin’ could have local processing to interpret data so reducing the data that needs to be sent for central processing, thereby making the system more efficient both in terms of hardware and energy.
Research Aims and Objectives:
The project will aim to create energy-efficient neuromorphic systems to enable AI on edges through the following objectives:
Methodology and Research Plan:
Expected Outcomes:
This project will contribute to advancements in both neuromorphic computing/engineering and AI for robotics, opening new avenues for developing energy-efficient, adaptive systems that can seamlessly integrate into real-world applications. Research outcomes include:
You will demonstrate excellent knowledge and skills in
Qualifications:
Applicants should have a minimum of a 2.1 Honours degree in a relevant discipline. An IELTS (Academic) score of 6.5 minimum (or equivalent) is essential for candidates for whom English is not their first language.
In addition to satisfying basic entry criteria, the University will look closely at the qualities, skills, and background of each candidate and what they can bring to their chosen research project in order to ensure successful and timely completion.
Any additional qualifications
You will demonstrate excellent knowledge and skills in
How to apply:
To apply, please complete the application form available from the following website: Computing and Information Science - MPhil, PhD - ARU via the above ‘Apply’ button. Please ensure the reference ‘PhD Studentship: Neuromorphic Edge AI for Robotics’ is clearly stated on the application form, under the title ‘Outline of your proposed research’. Within this section of the application form, applicants should include a 500-word outline of the skills that they would bring to this research project and detail any previous relevant experience.
Interested applicants should direct initial queries about the project to Professor Yonghong Peng via email: Yonghong.Peng@aru.ac.uk, or Professor Andrew Flewitt (University of Cambridge). For enquiries regarding the process and eligibility please contact SE-Research@aru.ac.uk.
Interviews are scheduled to take place in May 2025
We value diversity at Anglia Ruskin University and welcome applications from all sections of the community.
Closing Date: 09 May 2025
Funding Notes
A 3-year studentship is offered, intended to start in Sept 2025, providing a tax-free stipend of £19,237 per annum plus tuition fees at the UK rate. Due to funding restrictions, this studentship is only available as a full-time position and to UK candidates.
Project location: Cambridge campus.
Candidates for this PhD Studentship must demonstrate outstanding qualities and be motivated to complete a PhD within 3 years.
References
Bartolozzi, C., Indiveri, G. & Donati, E. Embodied neuromorphic intelligence. Nat Commun 13, 1024 (2022). https://doi.org/10.1038/s41467-022-28487-2
Krauhausen, I., Griggs, S., McCulloch, I. et al. Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics. Nat Commun 15, 4765 (2024). https://doi.org/10.1038/s41467-024-48881-2
Yao, M., Richter, O., Zhao, G. et al. Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip. Nat Commun 15, 4464 (2024). https://doi.org/10.1038/s41467-024-47811-6
Yang, Y., Bartolozzi, C., Zhang, H., Nawrocki, R.A., Neuromorphic electronics for robotic perception, navigation and control: A survey, Engineering Applications of Artificial Intelligence, 126, Part A, 106838 (2023). https://doi.org/10.1016/j.engappai.2023.106838.
Qualification Type: | PhD |
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Location: | Cambridge |
Funding for: | UK Students |
Funding amount: | £19,237 per annum |
Hours: | Full Time |
Placed On: | 4th April 2025 |
Closes: | 9th May 2025 |
Type / Role:
Subject Area(s):
Location(s):
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