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Denis Newman-Griffis

1 week ago

Fully Funded PhD in AI-Enabled Scientific Literature Analysis (NLP, Scientometrics, Metascience) University of Sheffield in United Kingdom

I am recruiting for a fully funded PhD studentship in AI-enabled scientific literature analysis at the University of Sheffield.

University of Sheffield

United Kingdom

email-of-the@publisher.com

Jan 15, 2026

Keywords

Computer Science
Data Science
Information Technology
Artificial Intelligence
Natural Language Processing
Citation Analysis
Doctoral Studies
Interdisciplinary Collaboration
Scientometric
Metaphysic
Machine learning

Description

A fully funded PhD studentship is available at the University of Sheffield, in partnership with Digital Science, focusing on AI-enabled analysis of scientific literature. The project aims to develop new tools and techniques using natural language processing, scientometrics, and metascience to analyze research papers and proposals, enhancing their connection to cross-disciplinary literature and improving science communication. The studentship is funded by the EPSRC DLA and covers full tuition (for UK or international students), a stipend, and research training and support. The successful candidate will join a world-leading team, including Denis Newman-Griffis and Mike Thelwall, and collaborate closely with Digital Science. Applicants should have a strong background in computer science, information technology, data science, or a related field, and an interest in NLP, machine learning, or scientometrics. The position starts in October 2026, and the application deadline is 15 January 2026. For more information and to apply, visit the provided link or contact the supervisors by email.

Funding

The studentship is fully funded by the EPSRC DLA, covering full tuition fees (for UK or international students), a stipend, and research training and support.

How to apply

Visit the provided application link to find out more and apply. Contact the supervisors by email if you are interested. Ensure your application is submitted by the deadline of 15 January 2026.

Requirements

Applicants should have (or expect to obtain) a strong undergraduate or master's degree in computer science, information technology, data science, or a related field. Experience or interest in natural language processing, machine learning, or scientometrics is desirable. International and UK students are eligible. No specific GPA or language test requirements are mentioned.

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