Value of award
100% of UK/EU tuition fees and an annual stipend of £14,553 (full award)
Number of awards
Start date and duration
January 2018 for three years.
Application closing date
20 December 2017.
This project will develop novel methods for interactive visualization and analysis of heterogeneous data from multiple sources.
In today’s era of big data, the most challenging and important tasks is to extract informative and valuable knowledge from the constantly increasing data deluge. There is a great need of efficient and usable analysis tools that support gaining of insight and address data analysis challenges. Data visualization utilize methods from HCI, computer graphics, data mining and other data science areas to support knowledge generation and bridge the gap between data and user.
A major challenge in big data analysis is the integrative analysis of heterogeneous data that is gathered from multiple sources. These data may be of different types, collected at different levels of detail and from different application domains; and it is expected that the integrative analysis across datasets may lead to greater insight. Such data is common in areas such as climate and weather research, combining for instance temperature data, pollution levels and demographical data, or bio-medical domains where data as diverse as genome sequences and environmental data may need to be combined.
Within this project you will develop and implement state-of-the-art visualization methods to support analysis of diverse multi-source data. The project may focus on one or several areas of relevance, including for instance:
- Visual representation of heterogeneous data (numerical, categorical, text, networks…)
- Visual representation of correlation and other relationships across multiple heterogeneous datasets
- Visual integration and coordination of data at different levels of detail
- Visual representation and analysis of the uncertainty generated through the combination of diverse data from different sources
- Visual analysis of diverse data on small display devices.
School of Computing, Newcastle University
Name of supervisor(s)
You should have either a First class honours degree or 2.1 in Computing Science, Mathematics, Electrical and Computer Engineering or other relevant science or engineering subject. A distinction level Masters degree in a related subject will be a plus. Equivalent experience will also be considered.
Good programming skills are desirable, and experience of data visualization, HCI and/or data mining.
The studentship is available to UK/EU applicants only.
How to apply
You must apply through the University’s online postgraduate application system. To do this please ‘Create a new account’.
All relevant fields should be completed, but fields marked with a red asterisk must be completed. The following information will help us to process your application. You will need to:
- insert the programme code 8050F in the programme of study section
- select ‘PhD Computer Science (FT) - Computing Science’ as the programme of study
- insert the studentship code cs063 in the studentship/partnership reference field
- attach covering letter, CV and (if English is not your first language) a copy of English language qualifications. The covering letter must state title of studentship, quote reference cs063 and describe how your research interests fit with the topic of research project outlined in the advertisement (max. two pages).
Please send your covering letter and CV by e-mail to email@example.com.