About the Project
This research will investigate machine learning using biologically inspired spiking neural networks. While much is known about how to train deep artificial neural networks, relatively little is understood about how the brain learns and processes information using electrical pulses known as 'spikes'. One feature of the brain that is well-understood, however, is its extremely low power consumption. Spiking neural networks are an evolution of the deep neural networks common in machine learning today, which promise to reduce drastically the energy footprint of AI systems, particularly when combined with neuromorphic hardware. This work will therefore explore bio-inspired AI algorithms suitable for this next-generation hardware.
The goal is to explore learning techniques observed in the brain, in combination with methods from the deep learning community, and their implementation in spiking neural network based AI algorithms. The research will target state-of-the-art solutions to traditional problems (e.g. image classification/segmentation and object detection, and natural language processing), and understanding of next-generation applications capable of exploiting neuromorphic principles. The work will research new paradigms such as online learning, developing new approaches to spatial and temporal credit assignment, and exploitation of principles from biology such as in-sensor and in-memory computing. Work will typically be simulation-based, making use of HPC/GPUs/neuromorphic hardware for modelling, and neural network description languages such as PyTorch, TensorFlow and other SNN modelling tools.
Please get in contact for more information and to discuss specific projects/applications.
oliver.rhodes@manchester.ac.uk
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.
Funding
At Manchester we offer a range of scholarships, studentships and awards at university, faculty and department level, to support both UK and overseas postgraduate researchers applying for competition and self-funded projects.
For more information, visit our funding page (https://www.se.manchester.ac.uk/study/postgraduate-research/fees-and-funding/) 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 supervisor for this project before you apply.
How to apply
Apply online through our website: https://uom.link/pgr-apply-fap
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:
If you have any questions about making an application, please contact our admissions team by emailing:
FSE.doctoralacademy.admissions@manchester.ac.uk.
Equality, diversity and inclusion (https://www.manchester.ac.uk/connect/jobs/equality-diversity-inclusion/) is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.
We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).
