Postdoctoral Assistant in Machine Learning: The Torr Vision Group at the Department of Engineering Science in central Oxford is seeking a full-time Postdoctoral Research Assistant supported by Professor Philip Torr’s Schmidt Science AI2050 fellowship for a two-year term. This position involves foundational research aimed at enhancing artificial intelligence capabilities in social sciences.
Postdoctoral Research Assistant in Machine Learning
Designation:
Postdoctoral Research Assistant
Table:
| Field | Details |
|---|---|
| Location | Central Oxford |
| Research Area | Machine Learning, AI, Social Sciences |
| Pay Scale | Research Grade 7 |
| Salary (£) | £39,424 – £47,779 per annum |
| Contact Email | philip.torr@eng.ox.ac.uk |
| Contact Person | Professor Philip Torr |
| Vacancy ID | 184269 |
Research Area:
- Foundational research on large-scale models and agentic architectures for social-science reasoning and planning.
- Causal reasoning methods over heterogeneous historical and social data, including texts, maps, images, and archaeological records.
- Building infrastructure and benchmarking for large-scale social-science simulation.
Eligibility/Qualification:
- PhD (or nearing completion) in Computer Science, AI, Security, or a related field.
- Demonstrable expertise in foundation models, large language models, multimodal modeling, or agentic/multi-agent systems.
- Strong knowledge of causal discovery methods and/or agent-based modeling for social sciences is essential.
- Excellent communication skills.
Job Description:
The postholder will contribute to multiple strands of research designed to lay a foundation for AI-enabled social science and historical analysis. Responsibilities include:
- Engaging in foundational research on AI technologies.
- Analyzing and simulating social-science data.
- Developing open, reproducible tools and datasets for community use.
How to Apply:
Candidates must submit an online application, including a covering letter/supporting statement that describes their research interests and how they align with this position, a CV, and details of two referees. Applications should be submitted via the official portal by the deadline.
Last Date to Apply:
Midday on February 9, 2026.
For further inquiries, please contact Professor Philip Torr via email.
