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EPSRC DTP PhD Studentship in Data Science

University of Reading - Department of Computer Science

Qualification Type: PhD
Location: Reading
Funding for: UK Students, EU Students
Funding amount: Tuition fees plus RCUK stipend for 3 years from September 2019
Hours: Full Time
Placed On: 10th January 2019
Closes: 4th March 2019
Reference: GS19-025

Project title:  Unsupervised Predictive Algorithms for Data Streaming Mining coping with unlabelled classes input

Supervisors:  Dr Frederic Stahl, Prof. Atta Badii

Project Overview:   The field of Data Stream Mining is concerned with the analytics of high velocity Big Data Streams. A data stream is a sequence of consecutive data instances that is infinite and generated in real-time. Thus applications, such as data mining can only read the sequence once using limited computing and storage capabilities. Predictive analytics is one of the most important types of data mining techniques, where an unknown variable in a dataset is predicted. For example, imagine a sequence of twitter posts that is generated in real-time. One application could be to predict if a tweet is related to a specific topic, e.g. politics. A data stream predictor would learn a model that can then be applied to new tweets in order to predict whether they are related to politics. Particular challenges here are the generation of data mining models that automatically adapt to changes of the pattern encoded in the stream (concept drift). In the example a concept drift could be “breaking news” related to politics which influences the topics which are being discussed on twitter.  Further application examples are detection of performance bottlenecks in computer networks or traffic congestion forecasting in smart cities.

The aim of this PhD project is to develop new cutting edge predictive analytics methods/algorithms for Big Data Streams that can forecast events ahead of time and adapt to concept drift. The project is in collaboration with an industry partner that will contribute real-world case studies and data stream processing infrastructure. 


  • Applicants should hold or expect to gain a minimum of a 2:1 Bachelor Degree or equivalent in Computer Science, Mathematics or related subject.
  • Due to restrictions on the funding this studentship is open to UK/EU students.

Funding Details:   

  • Starts September 2019
  • 3 - year award
  • Tuition fees plus RCUK stipend

How to apply:   

To apply for this studentship please submit an application for a PhD in Computer Science at

*Important notes*

  • 1) Please quote the reference ‘GS19-025’ in the ‘Scholarships applied for’ box which appears within the Funding Section of your on-line application.
  • 2) When you are prompted to upload a research proposal, please omit this step.

Application Deadline:  4th March 2019

Further Enquiries:  

Please note that, where a candidate is successful in being awarded funding, this will be confirmed via a formal studentship award letter; this will be provided separately from any Offer of Admission and will be subject to standard checks for eligibility and other criteria. 

For further details please contact Dr Frederic Stahl:, tel. +44(0)118 378 8983

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