PhD studentship: Integration of health informatics: ‘big data' for clinical translation in pa

United Kingdom
Feb 07, 2018
Feb 23, 2018
Organization Type
University and College
Full Time
Main Supervisor: Sarah Ennis

Other members of the supervisory team: Ben Macarthur, R Mark Beattie, Enrico Mossotto

Duration of the award: 36 months

Full time or part time: Full Time

Project description:

This project will involve the analysis of ‘big data’ in a cohort of children diagnosed with autoimmune disease. The supervisory group have access to one of the largest and best clinically characterised cohorts of children diagnosed and recruited through close partnership with Southampton Children’s Hospital . The project utilises detailed clinical characteristics for these paediatric patients in addition to ‘omic research data. Genomic data represent one of the central data types for analysis. We have applied next generation sequencing to sequence the whole exomes of these children in addition to generating transcriptomic, microbiome and metabolomic data. We have existing pipelines for processing these data and have applied methods to gather and integrate these diverse data types and apply machine learning methodology to better understand the molecular biology of disease. By using these next generation health data in this way, we aim to distinguish patient subgroups using new mathematical methods e.g. distinguish patients that present similarly in clinic by identifying those with disease caused by immune dysfunction versus those cause by a ‘leaky’ gut. Our goal is to better integrate and interpret complex data with a view to understanding what causes disease on a patient-by-patient basis. By working closely with the clinical teams, we always aim to bring our findings from bench back to bedside to help inform patient treatment.  

This PhD studentship application aims to train a talented postgraduate in key areas of bio- and health informatics, genomics and mathematical modelling of ‘omics data. The student will: apply programming skills to manipulate and process raw data through relevant pipelines; learn and apply critical procedures for data curation and quality control; generate and collate key output data from genomics, transcriptomics, microbiome and metabolomics; work at the interface between health informatics and maths to harmonise and integrate data for the training and testing of mathematical models; interpret and translate key findings back to clinical research staff and the wider scientific community. This will be performed under the guidance of senior academics

experienced in genomic informatics and advanced mathematical modelling and an internationally recognised clinical expert in paediatric IBD - all of whom have experience of successful PhD supervision.

Please contact: Sarah Ennis (

Person Specification: 

The successful candidate is expected to have either excellent qualifications in bioinformatics or a firm mathematical background, with a degree in a quantitative discipline such as mathematics, physical sciences, or computer sciences coupled with a strong interest in genetics and genomics of human disease. The project encompasses mainly “dry lab” experimentation and computational modelling but requires excellent communication skills to join a multidisciplinary team. Complementary skills in statistical analysis and computer programming will be a distinct advantage. A genuine excitement for, and interest in mathematical modelling of biological processes to address complex biomedical questions is essential for this project.

The successful candidate is likely to have the following qualifications:

Funding information:

This is 3-year full-time PhD and is a fully-funded to cover student fees and stipend (Research Council 2017/18 standard current rate £14,553.00).

Administrative contact and how to apply:

Please complete the University's online application form, which you can find at


You should enter Professor Sarah Ennis as your proposed supervisor. To support your application provide an academic CV (including contact details of two referees), official academic transcripts and a personal statement (outlining your suitability for the studentship, what you hope to achieve from the PhD and your research experience to date).


Informal enquiries relating to the project or candidate suitability should be directed to Sarah Ennis (

Closing date: Friday 23rd February

Interview date:  Wednesday 7th March

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