PhD studentship in Neuromorphic Neural Interfaces

This PhD studentship aims to study temporal dynamics in areas of the mammalian brain associated with debilitating disorders such as epilepsy and brain tumours. It will combine machine learning concepts and neuroscience theory to gain insight into how biological circuits function and uncover general principles which can then be encoded into hardware. It will also involve identifying, building, and validating machine learning methods to automate analysis of large-scale sensor datasets. The research scope is broad, and the project will be adapted based on the student’s interest (i.e. anomaly detection and unsupervised learning techniques for high dimensionality reduction are also areas of interest). We will be mainly working with neural datasets acquired from our partners at The Francis Crick Institute and Imperial College Faculty of Brain Sciences.

We welcome applications from talented individuals who should have an MEng/MSc (or equivalent, or near completion) with first class honours or distinction in Computational Neuroscience, Artificial Intelligence, Machine Learning, Physics, Electronics, or Neuromorphic Engineering. The successful applicant will demonstrate strong interest and self-motivation in the subject and excellent programming skills (in C++, Matlab, Python or equivalent). Previous research experience in a collaborative interdisciplinary research environment is desirable. Please contact Dr Romeo Racz (r.racz@southampton.ac.uk) for further details or to express an interest.