Post Doctoral Research Assistant (FRAME) (Fixed-Term) in Bournemouth

About the role

We are seeking to fill one position to undertake directed research activity as part of an EU project called FRAME (A Model-Based Foundry for Engineering, Adapting, and Assuring the Quality of AI Agents) under the direction of Prof Hamid Bouchachia. The project will deliver the engineering abstractions, methods and tools needed to support reliable, scalable end-to-end development, deployment and evolution of AI agents. By establishing foundations for trustworthy agent engineering, FRAME will enable productivity, adaptability, self‑improvement, reliability and foster large‑scale adoption. FRAME’s will be validated on real‑world use cases (robotics, healthcare and software development frameworks).

The research associate will be affiliated with the Data Science & Intelligent Systems based in the Computing & Engineering Department. The group is very dynamic, ambitious, well networked and delivers state‑of‑the‑art research in a range of machine learning and data science topics, publishing research results in prestigious venues.

The research work focuses on understanding and addressing the challenge of evolving real‑world data over time and open‑end learning in the context of self‑adaptive and self‑improving AI agents contributing to the development of methods for monitoring model behaviour, continuous evaluation, and improvement of agents across different training stages (e.g., pre‑training and fine‑tuning).

Depending on background, the applicant will pick one of the following topics:

• Developing novel indicators to capture distribution shifts, forgetting, transfer loss, etc. to monitor agent’s model performance and designing methods to recommend self‑improvement strategies based on such system performance indicators.
• Evaluation of the effectiveness of different adaptation strategies under varying conditions, investigating and leveraging meta‑learning and knowledge‑driven approaches to guide dynamic selection of self‑improvement techniques in response to evolving system requirements.
• Development and evaluation of continual learning techniques (e.g., continual pre‑training, domain‑adaptive pre‑training, fine‑tuning) and exploration of adaptive mechanisms for rapid adjustment, strategic retraining, and modular updates without full system redesign
• This is an excellent opportunity to gain hands‑on experience in, among other topics, evolving agentic systems, continual learning, model robustness, and adaptive AI systems. The applicant should be holder of a PhD in artificial intelligence/machine learning and possess excellent background in agentic systems, large language models and/or continual learning besides analytical, programming, communication and scientific writing skills contributing effectively and competently to the delivery of FRAME by designing and conducting the proposed research and producing published outputs.

The successful candidate will have access to funding for international travel, e.g., for attending conferences, attending the consortium meetings, and research dissemination, while working in a supportive, collaborative, inclusive and non‑discriminating working environment. We welcome candidates from all backgrounds to apply

Both positions are available on a fixed‑term basis for 15 months & 16 months.

About the department

Our vibrant Faculty of Media, Science & Technology encompasses a wide range of disciplines across four Schools: the School of Computing & Engineering, the National Centre for Computer Animation, The Media School and the School of Psychology. The breadth of subject areas in the Faculty means our cutting‑edge research is delivering real benefits to digital creative industries, simulation domains, cyber security, human centred artificial intelligence, cognitive science and many others. Our staff base includes high profile researchers who are involved in both UK and global consortia of externally funded collaborations, furthering knowledge in the aforementioned areas plus many more, thus providing significant impact to the economy as well as society and, ultimately, the quality of living across the globe.

About us

Bournemouth University’s vision is worldwide recognition as a leading university for inspiring learning, advancing knowledge and enriching society through the fusion of education, research and practice. Our highly skilled and creative workforce is comprised of individuals drawn from a broad cross section of the globe, who reflect a variety of backgrounds, talents, perspectives and experiences that help to build our global learning community.

BU values and is committed to an inclusive working environment. We seek a diverse community through attracting, developing and retaining staff from different backgrounds to contribute to inspirational learning, advancing knowledge and enriching society. To support and enable our staff to achieve a balance between work and their personal lives, we will also consider proposals for flexible working or job share arrangements.