Location: | Oxford |
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Salary: | £37,524 to £45,763 Grade 7 |
Hours: | Full Time |
Contract Type: | Fixed-Term/Contract |
Placed On: | 5th December 2024 |
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Closes: | 6th January 2025 |
Job Ref: | 176519 |
We are seeking a full-time postdoctoral researcher to join the Machine Learning Research Group at the Department of Engineering Science (central Oxford). The post is funded by the Oxford Martin School and is fixed-term to the 31st August 2026.
The successful candidate will work as part of a project team, consisting of researchers in the departments of Engineering and Computer Science, supporting the Oxford Martin Programme on AI Threat Detection, as well as engaging across the wide local network of experts in AI, cybersecurity, AI safety & governance. The Oxford Martin Programme on AI Threat Detection aims to fill a critical gap in AI security by developing advanced methods to detect attacks on AI systems.
You will be responsible for developing a test framework including a library of target AI models and training datasets. You will also help research the spectrum of threat and vulnerability models for the AI systems.
You should have a relevant PhD/DPhil or be near completion (submitted your thesis) together with relevant experience. You should also have previous experience with abnormality detection, or related machine learning techniques, for detecting unexpected patterns in large data sets.
Informal enquiries may be addressed to Steve Roberts (email: sjrob@robots.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 the 6th January 2025 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.
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