DPhil Studentship in Ultrasound Neuroimage Analysis

DPhil Studentship in Longitudinal Tracking of Fetal Brain Development from 3D Ultrasound Images

3.5-year D.Phil. studentship (note: EPSRC DTP standard awards are 3.5 years)

Supervisor: Dr Ana Namburete

The fully-funded studentship is to undertake research on machine learning algorithms to analyse ultrasound images of the developing fetal brain. It is jointly supported by the Royal Academy of Engineering and the EPSRC.

Ultrasound (US) imaging is one of the first steps in a continuum of pregnancy care. The resolution of modern US machines enables us to observe and measure brain structures, as well as detect cerebral abnormalities in fetuses from as early as 18 weeks. Recent breakthroughs in image analysis techniques (e.g. deep learning) introduce opportunities to automate routine clinical measurements, and develop new metrics to track spatial and temporal patterns of fetal brain development.

The aim of the project is to analyse and quantify volumetric development of fetal brain structures using machine learning techniques and data derived from 3D ultrasound images. The focus of this doctoral project will be the design, development, and testing of an image processing platform to automatically align the images and extract structural biomarkers. Once these algorithms have been developed, the data will be used to characterize temporal changes, to ultimately understand how individual brain structures evolve during the second and third trimesters of pregnancy. As a close partner of the Nuffield Department of Obstetrics and Gynaecology, our lab has access to the largest database of 3D fetal brain US images. The student will capitalise on this dataset to extract information about neurodevelopment from clinical image data.

S/he will be a member of the Biomedical Image Analysis Laboratory at the Institute of Biomedical Engineering and will be supervised by Dr. Ana Namburete. Further information on the group can be found here.

Award Value

The studentship covers course fees at the level set for UK/EU students plus a stipend (tax-free maintenance grant) of £13,863 p.a. for the first year, and at least this amount for a further two years.


This studentship is available to all applicants but the University fees are covered only at the UK/EU rate. Therefore overseas students would have to provide the difference between the UK/EU and the overseas student rates for University fees from some other source, such as a scholarship or personal funds.

Candidate Requirements

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

  • A first class honours degree in Engineering, Physics, Mathematics, Computer Science, or a related discipline
  • Excellent analytical, interpersonal, as well as written and oral communication skills in English
  • Strong programming skills (e.g. Python, C/C++, and/or Matlab) and applied mathematical skills

The following skills are desirable but not essential:

  • Experience in computer vision or machine learning demonstrated by a publication at an international conference or an international journal
  • Interest in interdisciplinary research

Application Procedure

Informal enquiries are encouraged and should be addressed to Dr Ana Namburete.

Candidates must also 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 ENGBM_ANUNA in all correspondence and in your graduate application.

Application deadline

Noon on 25 January 2019