Research Assistant in Machine Learning and Social Network Analysis

University of Oxford, United Kingdom

Updated: about 1 month ago
Location: Oxford, ENGLAND
Deadline: 22 Mar 2024

We are seeking a Research Assistant to work on an interdisciplinary project on exaggeration, cohesion, and fragmentation in on-line forums, led by Professor Janet Pierrehumbert (Oxford e-Research Centre) and Professor Xiaowen Dong (Oxford-Man Institute) at the Department of Engineering Science, Central Oxford.  The post is funded by the EPSRC and is fixed-term until 30 September 2024.

The overall aim of the project is to apply state-of-the-art natural language processing and machine learning techniques to study fragmentation and polarisation in online discussion forums. The specific role of the RA is to develop novel graph machine learning frameworks for the analysis and prediction of polarisation in Reddit discussion forums. In achieving this, we are particularly interested in the integration of linguistic information with social network information, by using techniques such as graph neural networks. The post holder is expected to be an active member of the project team and participate in regular project meetings.

You should hold a first degree in a relevant field (such as social data science, computer science, and engineering) together with relevant experience in machine learning. You should possess good knowledge of programming in Python.

Informal enquiries may be addressed to Professor Xiaowen Dong (email: [email protected]).

For more information about working at the Department, see www.eng.ox.ac.uk/about/work-with-us/

Please apply early. Applications will be reviewed on an ongoing basis and the post will close early should sufficient applications be received.

You are required to upload a covering letter/supporting statement, including a brief statement of research interests (describing how past experience and future plans fit with the advertised position), CV and the details of two referees as part of your online application.

The Department holds an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology.


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