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Research Studentship in Information Engineering

Research Studentship in Information Engineering

Project: Deep learning methods for brain-to-text decoding

3.5-year D.Phil. studentship 

Supervisors: Prof Oiwi Parker Jones

My lab is developing neural prosthetics for speech with the ultimate aim of restoring communication to paralysed patients. I have funding for a responsible and motivated DPhil candidate to work on software engineering. Your role will be to help develop relevant deep learning methods, as well as to support the development and maintenance of a core Python library for neural speech decoding. To warm up, we will implement standard techniques in the acoustic domain for endpoint detection, keyword spotting, and automatic speech recognition. We will then generalise these methods for use with brain (rather than acoustic) data as input, validating the results on neural recordings obtained while subjects listen to speech. Assuming state-of-the-art results, the best models will be released as baselines for an open machine learning competition. Time permitting, we will extend the methods to neural data obtained while subjects produce inner speech.


This studentship is funded through the UK Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership and is open to Home students (full award – home fees plus stipend). Full details of the eligibility requirements can be found on the UK Research and Innovation website.

Award Value

Course fees are covered at the level set for Home students (c. £8960 p.a.). The stipend (tax-free maintenance grant) is c. £17,668 p.a. for the first year, and at least this amount for a further two and a half years. 

Candidate Requirements

Prospective candidates will be judged according to how well they meet the following criteria:

  • A first class honours degree in Computer Science, Engineering, or related discipline
  • Excellent English written and spoken communication skills
  • Ability to write clean Python code at the collaborative project level
  • Familiarity with at least one major deep learning framework

The following are desirable but not essential:

  • Experience working with PyTorch
  • Experience working with Docker
  • Experience working on speech technology (e.g. speech recognition)
  • Experience working with signal processing (e.g. electrophysiological data)

Application Procedure

Informal enquiries are encouraged and should be addressed to Dr Oiwi Parker Jones (

Candidates must submit a graduate application form and are expected to meet the graduate admissions criteria.  Details are available on the course page of the University website.

Please quote 23ENGIN_OPJ in all correspondence and in your graduate application.

Application deadline: noon on 5 May

Start date: October 2023