About the Project
Supervisor: Dr Norman Poh
Co-supervisor: Dr David Windridge
Due to technological advances, addressing the growth in the size of data sets used by science, medicine and industry has become an increasingly central concern. In this PhD, the candidate will work towards advancing machine-learning and data-mining algorithms for processing big data by addressing some or all of the following issues: handling of sampling bias and structural noise, handling of under-sampled and miss-labelled data; handling of covariates or confounding factors; exploitation of temporal logics; and finally, efficient retrieval of related data queries. The techniques developed will be applied to a number of application domains, including healthcare records, bioinformatics, business data, and data collected from ubiquitous sensing.
The candidate will contribute towards building a critical mass of strategic competency in healthcare analytics within the Computing department, and will work in a multidisciplinary team involving clinicians and IT engineers.
Required experience and qualifications:
- Masters-level degree in computer science, mathematics, statistics, or machine Learning; or equivalent research/industrial experience in data analysis
- A good command of English, as well as open-mindedness and the will to collaborate within a team
Additional desirable features:
- A record of peer-reviewed publication
- Evidence of system implementation
- Knowledge of statistical packages and high-level programming such as Matlab, R, and Python
- Knowledge of databases and SQL
Shortlisted candidates will then be asked to apply formally through the link:
http://www.surrey.ac.uk/postgraduate/courses/computing/computing-phd/apply. Applications will be continually received until the position is filled.
Based on your current searches we recommend the following search filters.
Based on your current search criteria we thought you might be interested in these.