Role
This role offers the opportunity to both accelerate the adoption of these core ideas and to further advance them. Building on our BIG Argument for AI Safety Cases, you will conduct research to advance the safety of general-purpose AI systems as part of complex systems and sociotechnical contexts. You will also have the opportunity to engage with PhD students from the UKRI AI Centre for Doctoral Training in Safe Artificial Intelligence Systems (SAINTS) through joint research projects, training activities and co-supervision.
We are seeking researchers who can demonstrate the capacity to push the boundaries of safety science for the deployment of AI Foundation Models, rather than simply applying existing techniques. Please specify if you wish to apply for this role as a Research Associate or a Research Fellow.
Skills, Experience & Qualification needed
Grade 6 - Research Associate
1. First degree in Computer Science, Engineering, Psychology or cognate discipline
2. PhD, or in final stages thereof, in Computer Science, Engineering, Psychology, Safety Science or cognate discipline, or equivalent experience
3. Knowledge of AI Foundation Models, such as LLMs, LRMs, VLMs and World Models
4. High quality publications in safety science and system safety venues
5. Ability to research AI Foundation Models for use in safety-critical applications
6. Ability to work as part of a diverse and multidisciplinary team
7. Experience of carrying out both independent and collaborative research
8. Collaborative ethos
Grade 7 - Research Fellow
9. First degree in Computer Science, Engineering, Psychology or cognate discipline
10. PhD in Computer Science, Engineering, Psychology, Safety Science or cognate discipline, or equivalent experience
11. Knowledge of AI Foundation Models, such as LLMs, LRMs, VLMs and World Models
12. Ability to research AI Foundation Models for use in safety-critical applications
13. Ability to lead and/or take responsibility for a small research project or identified parts of a large project
14. Ability to supervise the work of others, for example in research teams or projects
15. Ability to work as part of a diverse and multidisciplinary team
16. Experience of carrying out both independent and collaborative research
17. Collaborative ethos
Interview date: w/c 18th May