Research Associate in Safe Reinforcement Learning

We are looking for motivated individuals to Join the Formal Methods in AI (FMAI) lab at Imperial College London, led by Dr. Francesco Belardinelli , in a fully funded postdoctoral research role to lead transformative research in formal methods for safe reinforcement learning.

Overview . The FMAI lab at Imperial is seeking highly motivated and talented Postdoctoral Research Associates (PDRAs/Post Docs), who have demonstrated competence in conducting cutting-edge research. The position is fully funded in the context of Dr. Francesco Belardinelli’s ARIA project Enforcing Safety in Cyber-Physical Systems via Proof Certificates, and focus on the design, development, and application of Safe RL algorithms as well as their verification via Proof Certificates
, including monitoring and shielding of cyber-physical systems.

AI-powered cyber-physical systems must operate continuously and reactively in safety-critical environments. Failures pose severe economic risks, even cost human lives.

In recent years, Safe RL has been developed to apply RL techniques in safety-critical environments. However, current methods primarily provide finite-horizon, statistical, or asymptotic guaranties, and fail to ensure strict safety compliance s creates a fundamental gap between scalable learning and certifiable safety.

To address this gap, this project aims at developing Certified Reinforcement Learning , a neuro-symbolic framework for learning safe controllers in real-world cyber-physical systems that leverages the scalability and adaptability of RL, while providing the formal, verifiable guaranties associated with Formal Methods.

The proposed methodology will be implemented in the MASA-Safe-RL library– an open-source platform for Safe RL currently being developed at the FMAI lab.

Within the project, y ou will conduct original research in the new and exciting field of Formal Methods for Safe RL and explore its applications across cyber-physical systems . You will develop novel algorithms that leverage proof certificates . In doing so, you will collaborate with a team of expert researchers in reinforcement learning, formal methods , strategy synthesis, multi-agent systems , and related fields.

We strive in publishing in top-tier conferences and journals.

• Self-driven and motivated individuals with genuine love for at least one of Formal Methods/Reinforcement Learning, possibly both, with a drive to learn about the other area.
• The applicant is also expected to have a strong track record in top conferences and journals in the field of Formal Methods/Reinforcement Learning, such as AAAI, AAMAS, IJCAI, NeurIPS, ICML, ICLR etc.
• We expect excellent skills in mathematics, especially knowledge in formal methods, stochastic systems and processes, the foundations of deep learning.
• Experience coding with deep learning libraries such as Pytorch/JAX is essential.
• Applicants must hold a PhD in computer science, mathematics or equivalent experience.

Please see job description for a full list of requirements.

• 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.

Full-time, Fixed term contract to start as soon as possible up to 31 st May 2028.

* Candidates who have not…