PhD Studentship: Towards Hardware-AI Enabled Neural Interfaces

Closing Date
31 Aug 2021
Salary
£15,609 tax-free per annum
Address
University of Southampton
Duration
3.5 years

Project Details

The brain produces in 30 seconds as much data as the Hubble Space Telescope in its entire lifetime. To disentangle and study this vast amount of information, and pave the way to new treatments to debilitating brain disorders, we developed a high-resolution neural interface. This deep-brain interface uniquely enables us to interrogate brain dynamics with single-cell resolution across any/all scales of the mammalian brain. With the prospect of hardware-AI acceleration, the interface can potentially learn and improve its function over time in a safe and energy efficient manner. This technology will be at the core of next-gen neuroprosthetics and brain machine interfaces aimed at restoring neural function such as hearing and vision, and treating drug-resistant brain disorders. 

This PhD studentship aims to study temporal dynamics in areas of the mammalian brain associated with epilepsy and brain tumours. You will combine machine learning concepts and experimental neuroscience to gain insight into how biological circuits function. You will work on identifying, building, and validating machine learning methods to automate analysis of large-scale neural sensor datasets. You will uncover general principles and encode them as basis of function of synthetic neural circuits. 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 working with in-vivo neural datasets acquired at our partners and collaborators at The Francis Crick Institute, Imperial College and Oxford University. 

Person Criteria

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent) or 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 or a closely related relevant subject. 

How to Apply

Applications should be made online. Select programme type (Research), 2021/22, Faculty of Physical Sciences and Engineering, next page select “PhD Nanoelectronics (Full time). 

Applications should include:

  • Research Proposal
  • Curriculum Vitae
  • Two reference letters
  • Degree Transcripts to date

Apply online: https://www.southampton.ac.uk/courses/how-to-apply/postgraduate-applications.page 

Contact Details

f you wish to discuss any details of the project informally, please contact Dr Romeo Racz, Centre for Electronics Frontiers, Email: r.racz@southampton.ac.uk, Tel: +44 (0) 75 2251 3577 

For further information please contact: feps-pgr-apply@soton.ac.uk

Further Information

Closing Date: Applications should be received no later than 31 August 2021 for standard admissions, but later applications may be considered depending on the funds remaining in place.

Funding: For UK students, Tuition Fees and a stipend of £15,609 tax-free per annum for up to 3.5 years.