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Advanced Machine Learning using Neuromorphic Spiking Neural Networks

Department of Electronic and Electrical Engineering

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Prof john soraghan , Dr G Di Caterina No more applications being accepted Funded PhD Project (European/UK Students Only)

About the Project

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 the theoretical underpinning of Neuromorphic SNN fundamentals with specific emphasis on unsupervised Neuromorphic SNNs and Neuromorphic SNN Reinforcement Learning strategies.
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 Notes

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

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