Postdoctoral Research Assistant in Machine Learning Closing date: Oct 09, 2026

University of Oxford, Department of Engineering Science; Oxford; On-site; GBP 39,000 - 48,000; Full time

Job summary

University of Oxford, Department of Engineering Science in Oxford invites applications for a full-time Postdoctoral Research Assistant in Machine Learning. The role is funded on a fixed-term basis to 30 June 2027 with a salary of £39,424 to £47,779 per annum. Closing date: Oct 9, 2026 at noon.

The successful candidate will research safety, security, control, and capabilities of advanced AI systems, focusing on agentic models interacting with operating systems, APIs, tools, and other agents.

Qualifications

• PhD (or near completion) in CS, AI, Security or related field.
• Strong ML and/or computer security background.
• Experience with LLMs or agent-based systems.

Responsibilities

• Investigate safety, security, control, and capabilities of advanced AI systems.
• Publish research findings and contribute to Torr Vision Group activities.

Skills

Machine learning

AI safety

Security

LLMs

Education

PhD in Computer Science or related field

Job description

Oxford, United Kingdom

Engineering Science

Postdoctoral Research Assistant in Machine Learning

Salary: £39,424 to £47,779 per annum

Closing date: Oct 09, 2026 12:00PM

We are seeking a full-time Postdoctoral Research Assistant to join Torr Vision Group at the Department of Engineering Science, central Oxford. The post is funded by Coefficient Giving and is fixed-term to the 30th June 2027.

We are seeking a highly motivated postdoctoral researcher to investigate the safety, security, control, and capabilities of advanced AI systems, with a particular focus on increasingly autonomous and agentic models. The research will examine frontier AI systems that interact with operating systems, APIs, software tools, other agents, and complex environments, moving beyond text generation towards systems capable of planning, pursuing objectives, and executing long-horizon tasks.

A central focus will be agentic safety, adversarial robustness, and AI control. This includes agent hijacking, prompt injection, adversarial manipulation, unsafe tool use, oversight evasion, and transferable attacks that generalise across agents, models, tasks, and environments. We are particularly interested in building agentic systems that can autonomously discover, test, and evaluate general attack strategies against other AI agents, helping identify systematic vulnerabilities rather than attacks specific to a single model or setting.

Candidates should possess a PhD (or be near completion) in Computer Science, AI, Security, or a related field. You will have a Strong background in machine learning and/or computer security and Experience working with LLMs or agent-based systems.

Informal enquiries may be addressed to [e-mail]

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

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

Keywords: Machine Learning, AI Safety, agentic safety

Contact: Adel Bibi, Email: [e-mail]