Postdoctoral Research Assistant in Machine Learning

We are seeking a full-time Postdoctoral Research Assistant in Machine Learning to join Torr Vision Group at the Department of Engineering Science (central Oxford). The post is fixed-term for 1 year.

This is an interdisciplinary research project aimed at advancing the interpretability, safety, and alignment of machine learning systems. We seek candidates with expertise into the use, extension and optimization of large language models (LLMs) and related architectures for generative tasks, continuous learning, indexing or retrieval, support of retrieval augmented generation over many data points from long term histories, automatic extraction of relevant data from noisy, multimodal observations.

This role offers the opportunity to develop new techniques for interpretable, safe, and socially beneficial AI while engaging with policy proposals and governance for responsible AI development. We seek researchers passionate about steering AI progress towards transparent, ethical, and human-aligned systems. There will be close collaboration with policymakers to apply empirical safety research for AI regulation and governance.

You should possess a PhD or DPhil (or near completion of) in Machine Learning. You should have knowledge of approaches for areas related to RAG, LLM, Agentic systems. You should have the ability to manage your own academic research and associated activities.

Informal enquiries may be addressed to Professor Philip Torr (email: philip.torr@eng.ox.ac.uk)

For more information about working at the Department, see

www.eng.ox.ac.uk/about/work-with-us/

Only online applications received before midday on 6th August 2026 can be considered. You will be 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.

Additional Actions :

Robotics Nano Technology, Linus, Machine Learning, Large Language Models, AI Safety