Higher Degree by Research Application Portal

Pulsars, rapidly rotating neutron stars emit periodic radio signals and represent some of the most extreme objects in the known universe. Detecting these rapidly varying signals in radio telescopes occurs very early in the overall signal-processing pipeline and is therefore a real-time extreme computational challenge. Traditional detection methods struggle to balance detection performance with computational performance, necessitating innovative approaches to handle what is essentially a detection challenge in a rapid spatio- temporal signal. Current state-of-the-art approaches leverage FPGAs and GPUs (often together) in the search for the highest levels of accuracy and energy efficiency.

Neuromorphic computing and Spiking Neural Networks (SNNs) present a compelling approach to this challenge. By emulating the time-varying behaviour of biological neurons, SNNs are uniquely suited to processing complex, dynamic data, such as audio signals or event-based video streams. SNNs are a very new technology for radio astronomy, with only few published results to date. This project will investigate the potential of SNNs for real-time, scalable pulsar detection, aiming not only to overcome the limitations of conventional methods but also to extend the boundaries of SNN applications to novel and demanding astrophysical tasks. 

This is an opportunity for a PhD in a rapidly growing area in Machine Learning. Spiking neural networks (SNNs) much more closely mimic the operation of a real brain than traditional artificial neural networks (ANNs) by reproducing the spike-driven nature of real neurons. There is also the opportunity to collaborate with SynSense AG, a startup developing neuromorphic hardware to execute SNNs at low power.

Please contact Andreas Wicenec in the first instance.

The ideal candidate will hold a Bachelor Honours or Masters degree in Astronomy with some experience in compu- tational methods; or a Bachelor Honours or Masters degree in Computer Science with an interest in Astronomy and High Performance Computing.

Skills

• Python

• Pytorch, Tensorflow or other Deep Learning experiences favoured

• High Performance computing environment experience (Slurm, MPI) also appreciated

Training and Development

The candidate will be responsible for designing and evaluating SNN models and learning how to perform large-scale SNN simulations and deep learning experiments on HPC platforms. Possibility of collaboration with SynSense, a provider of neuromorphic hardware. 

The Doctor of Philosophy (PhD) is a program of independent, supervised research that is assessed solely on the basis of a thesis, sometimes including a creative work component, that is examined externally. The work presented for a PhD must be a substantial and original contribution to scholarship, demonstrating mastery of the subject of interest as well as an advance in that field of knowledge. 

Visit the course webpage for full details of this course including admission requirements, course rules and the relevant CRICOS code/s.