Advanced Machine Learning using Neuromorphic Spiking Neural Networks

University of Strathclyde

Department of Electronic and Electrical Engineering

Project Description

A 3-year PhD research studentship is 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. 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.
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.

Funding Information

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.

Application Process

Candidates should submit their CV, academic transcript, and a covering letter outlining their suitability for the position, to Professor John Soraghan on [email protected]. Following a review of the application submissions, selected candidates will be invited for an interview. The application submission deadline is 17th Aug 2020.

To apply for this PhD, please email

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