Research Assistant / Research Associate in Leveraging Ontological Knowledge with Argumentative [...]

Research Assistant / Research Associate in Leveraging Ontological Knowledge with Argumentative Agentic AI to Accelerate Chemical Development

This post of Research Assistant (pre-doctoral) or Research Associate (post-doctoral) is to conduct world-leading research on Leveraging Ontological Knowledge with Argumentative Agentic AI to Accelerate Chemical Development under the direction of Prof Francesca Toni, in collaboration with Prof Alexei Lapkin from the University of Cambridge, Dr Antonio Rago from King’s College London, and industrial collaborators, within the AIChemy Hub ( https://aichemy.ac.uk/ ). The successful candidate will focus on developing an advanced argumentative agentic AI approach for chemical development settings based on reinforcement learning but able to shape rewards with the help of ontological knowledge of chemical process development (by the group of Prof. Lapkin based in Cambridge and in Singapore) as well as expert knowledge from humans and from other agents. The approach will be based on and will develop techniques from computational argumentation combined with reinforcement learning . The argumentative agents will be able to interact with other such agents using methodologies for argumentative exchanges developed at King’s College London.

What you would be doing

The position offers an exciting opportunity for conducting internationally leading and impactful research in AI. The Research Assistant/Associate will be responsible for researching, shaping and delivering solutions based on argumentative agentic AI in a variety of settings (formats, models and data), and will be expected to submit publications to top-tier conferences and journals in AI as well as Chemistry and Chemical Engineering.

The successful applicant will join the CLArg research group, led by Prof Toni: this is a friendly, vibrant, multi-national team working on various aspects of argumentation-based explainable AI (for further information on the group and related projects, see: https://clarg.doc.ic.ac.uk ). The successful applicant will need to interact and collaborate with the other partners from the University of Cambridge, King’s College London and industry, as well as to engage with others with expertise in AI and/or chemistry.

What we are looking for

• A strong computer science background with a focus on AI, have experience, including a proven publication track‑record, in at least two of the following areas, as well as ability and willingness to become familiar with the others: reinforcement learning, computational argumentation, multi‑agent systems, ontologies.
• A Master’s degree (Research Assistant) or PhD degree (Research Associate) in computer science or a related area.
• Familiarity with standard reinforcement learning libraries/data analysis.
• Excellent communication skills and ability to work with others.
• Ability to organise your own work and set priorities to meet deadlines.
• Willingness to travel to conferences and meetings.

What we can offer you

• The opportunity to continue your career at a world‑leading institution and be part of our mission to continue science for humanity.
• Grow your career: gain access to Imperial’s sector‑leading dedicated career support for researchers as well as opportunities for promotion and progression.
• Sector‑leading salary and remuneration package (including 41 days off a year and generous pension schemes).
• Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing.

Further information

Full‑time, Fixed‑term contract to start July 2026 or soon after for up to 24 months.

Candidates who have not yet been officially awarded their PhD but are in the process of obtaining one will be appointed as Research Assistant within the salary range £43,863‑£47,223 per annum.

In addition to completing the online application candidates should attach:

• A full CV with a list of all publications
• A 2‑page research statement indicating what you see as interesting research issues relating to the above post and why your expertise is relevant.

Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above.

We reserve the right to close the advert prior to the closing date stated should we receive a high volume of applications. It is therefore advisable that you submit your application as early as possible to avoid disappointment.

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