AI for precision medicine: tackling missing measurements in high-dimensional data to improve disease diagnostics and treatment

Today scientists and clinicians use massive datasets, such as genetics and MRI images, to understand human health and disease. While there has been development in analysing such data, addressing missing data is often overlooked. We have developed methods to address missing data from two different perspectives: 1) from biobank research, a random forest method for many individuals, and 2) from our dementia research, a Bayesian method working on fewer individuals. The PhD student will apply and evaluate these methods to MRI imaging data from our collaborators and UK biobank and develop tools to provide solutions for imputation of missing data for research and clinical practice.

The candidate will benefit from the newly launched AI Institute and its collaborative networks and training possibilities. The University of Surrey also offers a variety of other professional training courses that will be made available to the successful applicant. The existing collaborative network will provide the candidate exciting opportunities within UK.

Open to any UK or international candidates.

All applicants should have (or expect to obtain) a first-class degree in a numerate discipline (mathematics, science or engineering) or MSc with Distinction (or 70% average) and a strong interest in pursuing research in this field. Additional experience which is relevant to the area of research is also advantageous.

English language requirements: IELTS Academic 6.5 or above (or equivalent) with 6.0 in Writing, or equivalent.