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UCL

Moving the Frame: New Computational Practices for the BT Film Collection

University College London Department of Information Studies
✓ Fully Funded ⏰ Closing Soon machine learning data science computer vision artificial intelligence british and irish history digital media film studies librarianship

Leverage advances in machine learning and computer vision to transform metadata creation for heritage film archives. Develop innovative, reusable computational tools that enhance archival discoverability and research.

AI-generated overview

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Why This Research Matters

This research unlocks valuable historical audiovisual collections by automating complex cataloguing tasks, making cultural heritage more accessible and analyzable. It addresses ethical and practical challenges of AI in archives, setting standards for future digital archival projects.

Machine Learning Computer Vision Knowledge Organisation Digital Archives Cultural Heritage

Project Description

The BT Group Archives and UCL are collaborating on a fully funded PhD to develop new computational methods for the extensive moving image archive spanning eighty years of British communication history. This collection, starting from the 1930s GPO Film Unit, captures transformations in communications and the Information Society through the lens of a major British corporation. The project addresses labor-intensive archival cataloguing challenges by exploring advances in machine learning, computer vision, and audio recognition to create rich, linked metadata for the films. The student will contribute new research methods and insights to shape digital archival practice. The student will conduct a critical investigation into computational approaches for knowledge organisation, working closely with supervisors at BT Group Archives and UCL. Hands-on experience at both institutions will include engaging with archive staff, in-house training, and developing practice-based outputs. Outputs may include new comprehensive metadata, publishable code, documentation, workshops, and teaching materials. The thesis will integrate these practical contributions with a substantive research paper, providing transferable methods for archives using AI. This project enhances discoverability and accessibility of a major audiovisual heritage collection, addressing broader challenges of applying AI in archival contexts. It supports innovative research training at UCL, networks via the AHRC Collaborative Doctoral Partnership scheme, and develops skills and knowledge valuable to the cultural heritage sector.

Entry Requirements

Applicants should be graduates and have or expect to receive a Masters-level qualification in a relevant subject or discipline including, but not limited to: archival studies, library and information studies, history, film or media studies, computer or data science, computer vision, multimodal AI, digital humanities or cultural analytics; or, be able to demonstrate equivalent experience in a cultural heritage institution or other relevant professional setting.

Applicants must be able to demonstrate an interest in archives and digital cultural heritage and the potential and enthusiasm for developing their technical skills in related areas.

UCL and BTGA are keen to encourage the widest range of potential students to study for this studentship. We particularly welcome applications from people of Global Majority backgrounds as they are currently underrepresented in doctoral student cohorts.

How to Apply

For this PhD at University College London:

Prepare your documents

You need to get these ready:

CV
Academic transcripts
Personal statement / cover letter
Research proposal (if requested by UCL portal)
Proof of English
Visit the website for more information

Eligibility

UK/Home
EU
International

Supervisor Profile

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Dr Daniel Wilson, Prof Andrew Flinn
University College London, Department of Information Studies

Dr Daniel Wilson and Prof Andrew Flinn are scholars at UCL’s Department of Information Studies, focusing on digital humanities, archival studies, and AI applications in cultural heritage. They lead interdisciplinary research that combines theory and practice to innovate in knowledge organisation and digital asset management. Their work is internationally recognized for advancing new methods in archival science and information studies.