PhD Studentship: Novel Neuromorphic, Radically Energy Efficient Training Algorithms for Action Recognition

Updated: 2 months ago
Job Type: FullTime
Deadline: 21 Apr 2023

Tuition fees and stipend at the standard Research Council rate (Home rate only: £4,596 (fees) and £17,668 (stipend) in 2022/23). The 2023/2024 rate is yet to be announced by the UK Research Councils.


Applications must be received by Friday 21 April 2023, 23:59 GMT


Open to home fee paying students only.

Scholarships are available on a cotutelle (dual award) basis only.

Students have to spend at least 12 months at Kent and Lille.

Key attributes and skills for prospective applicants:

To be successful, the applicant needs to have a first class undergraduate or master-level degree in computer science or a related subject (Statistics, Electrical engineering, etc.), ideally with a specialisation in machine learning.

While this is not a software engineering project, strong skills in programming will be required.

Further details


  • Dr Dominique Chu, School of Computing, University of Kent
  • Professor Pierre Tirilly, Faculté des sciences et technologies, University of Lille

This project will develop novel algorithms for Spiking Neural Networks to detect gestures and actions in videos. The student will be jointly supervised by Prof. Pierre Tirilly  at the Cristal Laboratory of the University  of Lille and Dr. Dominique Chu  at the School of Computing , University of Kent. The successful applicant will spend at least one year in Lille. The degree will lead to a dual doctoral award from Kent and from the University of Lille.

Project specifics:

Computing-related activities account now for a double digit percentage of the total global electricity consumption. Artificial Intelligence (AI) and especially the training of Neural Networks (NN) are known to be highly energy intense. In its current form, AI is thus environmentally unsustainable. At the same time, AI in general and NNs in particular have become societally important technologies and will also be important tools to find solutions to large-scale problems such as climate change. There is thus an urgent need to find ways to reduce the energy consumption of AI algorithms. 

A possible solution to the energy consumption of NNs is to use neuromorphic hardware to train them. Such hardware is radically more energy efficient than general purpose computers and GPUs. However, it requires a variant of NNs, so-called spiking neural networks (SNN), which are less researched than standard NNs. Moreover, the backpropagation algorithm cannot be run on this hardware. There are some alternatives that are compatible with neuromorphic hardware, but these currently underperform relative to backpropagation. For this project, we will develop novel algorithms to train SNNs on neuromorphic hardware to achieve performances that are comparable to backpropagation. 

There will be the opportunity to test the algorithms on neuromorphic hardware.

For question about this project, please contact Prof. Tirilly ( ) or Dominique Chu ( ).

How to apply

To apply please visit:  (Computer Science PhD programme).

You will need to apply through the online application form on the main University website. Please note that you will be expected to provide personal details, education and employment history and supporting documentation (Curriculum Vitae, transcript of results, a writing sample e.g. project report, Bachelor/Master thesis, two academic references). Applications should state that you would like to be considered for this Kent-Lille studentship project and be submitted with a supporting statement from the Kent Lead Supervisor.

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