Postgraduate research opportunities

Advanced Machine Learning using Neuromorphic Spiking Neural Networks

Two 36 month PhD studentships fully funded by the US Air Force Office for Scientific Research (US AFOSR) are available focussing on fundamental and algorithmic research into novel Neuromorphic Spiking Neural Networks models for processing event data streams from multiple UAVs.

Number of places



Home fee, Equipment costs, Travel costs, Stipend


22 June 2020


17 August 2020


36 months


To be considered for the project, candidates must possess an MEng/MSc (or equivalent, or near completion) with at least a 2.1 class honours or merit in Engineering, Computer Science, Mathematics, Physics, or a closely related subject.  Please note the funding available is restricted to UK Nationals only and adhere to Research Council (RCUK) eligibility criteria.

Eligibility for RCUK studentships

  • Research Council (RC) fees and stipend can only be awarded to UK and EU students and not to EEA or International students.
  • EU students are only eligible for RC stipend if they have been resident in the UK for 3 years, including for study purposes, immediately prior to starting their PhD.
  • If an EU student cannot fulfil this condition then they are eligible for a fees only studentship.
  • International students cannot be funded from RC funds unless they are ‘settled’ in the UK. ‘Settled’ means being ordinarily resident in the UK without any immigration restrictions on the length of stay in the UK. To be ‘settled’ a student must either have the Right to Abode or Indefinite leave to remain in the UK or have the right of permanent residence in the UK under EC law. If the student’s passport describes them as a British citizen they have the Right of Abode.
  • Students with full Refugee status are eligible for fees and stipend.

Project Details

Two fully funded 3-year PhD research studentship are available in advanced machine learning to investigate novel Neuromorphic (brainlike) Spiking Neural Network (SNN) models for processing event data streams from multiple UAVs for problems such as situational awareness, object classification, semantic segmentation, and tracking. 


Studentship 1:  The work will focus on the theoretical underpinning of Neuromorphic SNN models with specific emphasis on unsupervised Neuromorphic SNNs and Neuromorphic SNN Reinforcement Learning strategies.


Studentship 2:  The work will focus on engineering/computing and system design aspects of neuromorphic SNN models for particularly challenging areas relating to multiple UAVs.  Hybrid DLN /SNN will also be investigated.


The work will build on the considerable expertise that exists within the Neuromorphic Sensing Processing Laboratory in the Department of Electronic and Electrical Engineering.  As well as collaborating with teams working in Neuromorphic Technologies in the US Air Force Research Laboratory, the PhD student will also be engaged with researchers in core neuromorphic technology providers such as Intel’s Neuromorphic Research Community (INRC) and Advanced Brain Research.

Funding Details

Funding is provided for full tuition fees, along with a generous tax-free stipend and support with a Research Training Support Grant for research consumables and conference attendance.


The research will be supervised by Professor John Soraghan and Dr Gaetano Di Caterina who are Co-directors of the Neuromorphic Sensing Signal Processing Laboratory in the Department. Their main research interests are signal and image processing, machine learning theories, algorithms, with applications to radar, sonar and acoustics, biomedical signal and image processing, video & speech analytics, and condition monitoring. They have supervised 55 researchers to PhD graduation and have published over 350 technical publications.

Further information

Applications may be considered after the deadline.

Contact us

Professor J Soraghan

Email, or tel: +44(0)7960246196.

How to apply

Candidates should submit their CV, academic transcript, and a covering letter outlining their suitability for the position, to Professor John Soraghan on Following review of the application submissions, selected candidates will be invited for interview. Application submission deadline is 17 August 2020.


Interested candidates can also email, or tel: +44(0)7960246196 for an informal discussion.