"Postdoctoral Research Associate or Fellow"

About us

The Department of Informatics at King’s College London is looking to appoint a Postdoctoral Research Associate or Fellow to work in the area of technical AI safety.

About the role

The position is funded by the Open Philanthropy grant “Verifiably Robust Conformal Probes”. The project’s goal is to develop methods for latent probing (aka activation monitoring) of large language models (LLMs) that leverage certification and conformal prediction techniques to offer probabilistic and adversarial robustness guarantees. Applications include detection of misaligned LLM intentions such as deception, harmfulness, jailbreaking, and power-seeking behaviours.

You will work alongside Dr Nicola Paoletti and a core team that includes a PhD student from Dr Paoletti’s lab, Prof Osvaldo Simeone from the Department of Engineering at King’s College London, and Dr Mehran Hosseini from the University of Manchester. You will join a vibrant research environment, with several researchers in Dr Paoletti’s lab working on related topics, within a department that has a strong track record in AI research.

Applicants for the associate position should have already completed, or be close to completing, a PhD in a relevant discipline. Applicants for the fellow position should also have prior post-doc experience. Commensurate with their career stage, applicants should have an outstanding research track record in topics related to the project’s scope, such as mechanistic interpretability of LLMs, robustness verification of machine learning models, and conformal inference.

Applicants should demonstrate scientific creativity, research independence, the capacity to support junior team members, and strong communication skills, both written and verbal.

This is a full time post (35 hours per week), and you will be offered a fixed term contract until 31 October 2027 and starting as soon as possible.

Research staff at King’s are entitled to at least 10 days per year (pro-rata) for professional development.

About you

To be successful in this role, we are looking for candidates to have the following skills and experience:

Essential criteria (Grade 6, Research Associate)

• PhD in relevant subject area (or near completion of)
• Research experience in relevant research techniques and methodologies
• Strong publication record commensurate with the applicant’s career stage.
• Algorithmic and model development skills, and a familiarity with relevant machine learning libraries.
• Ability to work independently and within a team
• Effective communication: written and verbal

Desirable criteria (Grade 6, Research Associate)

• Creativity and originality in research
• Willingness to engage in dissemination activities beyond conference presentations (e.g., present in lab meetings or research visits).

Essential criteria (Grade 7, Research Fellow)

• PhD in relevant subject area
• Prior post-doc experience
• Research experience in relevant research techniques and methodologies
• Strong publication record commensurate with the applicant’s career stage.
• Algorithmic and model development skills, and a familiarity with relevant machine learning libraries.
• Ability to work independently and within a team
• Effective communication: written and verbal

Desirable criteria (Grade 7, Research Fellow)

• Experience supporting junior colleagues (e.g., MSc and PhD students)
• Creativity and originality in research
• Willingness to engage in dissemination activities beyond conference presentations (e.g., present in lab meetings or research visits).

Please note that this is a PhD level role but candidates who have submitted their thesis and are awaiting award of their PhDs will be considered. In these circumstances the appointment will be made at Grade 5, spine point 30 with the title of Research Assistant. Upon confirmation of the award of the PhD, the job title will become Research Associate and the salary will increase to Grade 6.

Grade and Salary: £45,031 to £58,426 per annum, including London Weighting Allowance

Job ID: 127005

Close Date: 20-Nov-2025

Contact Person: Dr Nicola Paoletti

Contact Details: nicola.paoletti@kcl.ac.uk