About the Project
Project summary
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)
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).
We are seeking talented and enthusiastic PhD candidates who will join our team working on next-generation agentic systems capable of adapting to dynamic environments. 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 (for example, pre-training and fine-tuning)
Postgraduate Researcher development opportunities
The researcher will have the opportunity to collaborate European partners part of the FRAME project. They 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.
Your duties
As a PhD student, you should expect to do the following:
- Plan, design, conduct research, and report the work in the form of publication and official reports.
- Deliver research objectives of proposed work within the timeframes of the project and ensuring the achievement of project outcomes.
- Maintain accurate, complete, and timely data and records in accordance with the project’s data management protocols.
- Present findings at national/international conferences and other dissemination and knowledge exchange activities, including public engagement.
- Be a point of contact for the project, liaise with colleagues from project partners as required, participate in the consortium meetings and report progress.
- Undertake personal and professional development activities in line with agreed appraisal and development programme to enhance personal knowledge and contribution to relevant activities.
More about the project
Depending on their 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 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 continual learning techniques (for example, 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 applicants should be holder of a Master 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.
More details and how to apply: PhD Studentship - Development and evaluation of continual learning techniques for multi-modal agent self-improvement | Bournemouth University
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