We have a number of funded PhD positions in INSIGHT National Centre for Data Analytics @ NUI Galway. The positions will focus on research challenges in data-mining, social network analysis and machine learning.

Topic overview

The widespread use of the social web and mobile devices and the availability of large collaboratively-maintained, multi-domain knowledge sources such as Wikipedia and dbpedia have created the potential to consolidate and link data from several sources into enormous heterogeneous networked data sets consisting of nodes and edges with inherent semantic meaning, interdependence and provenance.

Such data poses several challenges in influence measurement, predictive analytics, information propagation modelling, similarity calculation and clustering, structural role analysis, the detection of dependent concepts and latent relationships, real time analytics, the use and extraction of network schemas.

The research will be applied in the following domains (amongst others)

  • Novel scientometrics based on analysis of social media and Web data
  • Real time social media analytics for large customer support centres
  • Real time recommendation of knowledge/related topics for media outlets with large archives
  • Social media analytics to identify influential users and their behaviours in the domain of digital media broadcasting

The positions will involve collaboration with the following companies:

 Some of the core challenges will be :

  • Novel approaches to  Social Network Analysis and Graph Mining on heterogeneous networks to uncover dependent concepts, labels, latent relationships and for causality exploration
  • Scalable and interpretable user/group role analysis and modelling
  • Graph-based real-time recommendation approaches using Linked Data as a source of open background data
  • Spatio-temporal correlation analysis on linking patterns to uncover root causes of changes to network structure
  • Predictive models of social network evolution and significant events such as churn, community formation
  • Stream-computing models for mining massive heterogeneous graphs  – for example graphs combining social and linked-data
  • Hybrid graph mining techniques combining statistical and logic, inductive and deductive approaches

How to apply:

Applicants should have an excellent primary degree in computer science, maths or a relevant discipline (e.g. computationally focused social science). A master’s degree would be a benefit.  Applicants are asked to familiarise themselves with our previous research and to write a letter of introduction explaining their interest in the research we conduct and why they believe they are suitable for the position.

Each application should only contain a CV and the letter of introduction  – please do not include references, testimonials, certificates, university grades etc. We may request these later.

Applications should be sent to with the subject line INSIGHTSW3. Potential applicants are encouraged to submit as soon as possible early. Please feel free to contact us if you have any questions about the positions.