PhD Studentship in Population-scale Healthcare Analytics

University of Surrey - Department of Computer Science

Position Summary

Applications are invited for a cutting-edge 3-year PhD project in healthcare analytics, hosted in the Department of Computer Science, University of Surrey, UK.

Electronic health records contain a wealth of information that has not yet been fully exploited. Today, it is possible to retrieve millions of patient records over time, across vendors and care professionals from primary, secondary, to tertiary care.

This research project aims to develop a set of statistical tools for processing population-scale linked health records, that is, in the order of millions of patient records which are joined up from different care establishments and possibly other external data sources. Although a number of statistical software packages exist, there is still room for very substantial improvement, e.g. by designing better algorithms for handling sampling bias, structural noise, irregularity, and under-sampling of data; for handling covariates or confounding factors; exploiting temporal logics; and, not least, for efficient retrieval of similar patient profiles.

The work will concentrate upon novel pattern recognition, machine learning, and data-mining techniques. Knowledge and experience in multi-task learning, hierarchical modelling, statistical adaptation, kernel or other non-parametric methods, and/or deep-learning are desirable.

Department of Computer Science

The Department of Computer Science at the University of Surrey, within the Faculty of Engineering and Physical Sciences, has an international reputation for research and teaching. In the National Student Survey of 2015/16, overall student satisfaction was 95%. Research in the department is focussed on two main areas: Nature Inspired Computing and Engineering, and Secure Systems, with Surrey recognised by GCHQ as one of only thirteen Academic Centres of Excellence in Cyber Security Research. Its security related research is focused on protocol analysis, security verification, trusted computing, data privacy, access control, privacy-preserving architectures, vulnerability analysis, distributed ledger technologies, digital forensics, and human-centred computing.


The candidate will contribute towards building a critical mass of competency in healthcare analytics within the Department of Computer Science. He/she will be embedded in the MRC CKD research team currently consisting of two full-time postdoctoral MRC Research Fellows working in a multidisciplinary team involving clinicians and IT engineers. The team is part of the larger Nature Inspired Computing Engineering group with expertise in machine learning and optimization.


To apply you should have at least an upper second class honours degree in Computer Science, or a suitable hard science or other engineering subject. Preference will be given to those with appropriate MSc or equivalent research/industrial experience in data analysis and/or machine learning. It is not mandatory to have the experience of working with clinical/health/biology data but this can be advantageous.

The candidate is expected to be able to use or modify existing statistical tools or methodologies in order to solve novel problems posed by healthcare analytics. Familiarity with Matlab, Python, R, or SPSS is desirable.

In addition, you must have good communication skills and be fluent in English. We look for a candidate that is self-motivated, engaging, and is a team player.

Application Procedure

For further information regarding the application procedure and funding situation, please visit

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South East England