Overview. The FMAI lab at Imperial is seeking highly motivated & talented Postdoctoral Research Associates (PDRAs/PostDocs), 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 & 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 at runtime. This creates a fundamental gap between scalable learning & 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.

What you would be doing:

Within the project, you will conduct original research in the new & 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, & related fields. We strive in publishing in top-tier conferences and journals.

What we are looking for:

• 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 & 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.
• Fluent written and spoken English skills as well as contributions to the group culture are expected.
• Applicants must hold a PhD in computer science, mathematics or equivalent experience.

Please see job description for a full list of requirements.

Further Information

Full-time, Fixed-term contract to start ASAP up to 31^st May 2028.

*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £45,399 - £48,876 per annum. 

Visit https://www.imperial.ac.uk/jobs/  and search vacancy reference ENG04023.

In addition to completing the online application candidates should attach: 

• A full CV with a list of all publications
• A 1-page research statement indicating what you see are interesting research issues relating to the above post and why your expertise is relevant.

Informal enquiries should be directed to:

Dr Francesco Belardinelli francesco.belardinelli@imperial.ac.uk

Closing Date: 20^th September 2026 (midnight)

£50,733 to £59,484 per annum

Overview. The FMAI lab at Imperial is seeking highly motivated & talented Postdoctoral Research Associates (PDRAs/PostDocs), 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 & 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 at runtime. This creates a fundamental gap between scalable learning & 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.

What you would be doing:

Within the project, you will conduct original research in the new & 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, & related fields. We strive in publishing in top-tier conferences and journals.

What we are looking for:

• 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 & 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.
• Fluent written and spoken English skills as well as contributions to the group culture are expected.
• Applicants must hold a PhD in computer science, mathematics or equivalent experience.

Please see job description for a full list of requirements.

Further Information

Full-time, Fixed-term contract to start ASAP up to 31^st May 2028.

*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £45,399 - £48,876 per annum. 

Visit https://www.imperial.ac.uk/jobs/  and search vacancy reference ENG04023.

In addition to completing the online application candidates should attach: 

• A full CV with a list of all publications
• A 1-page research statement indicating what you see are interesting research issues relating to the above post and why your expertise is relevant.

Informal enquiries should be directed to:

Dr Francesco Belardinelli francesco.belardinelli@imperial.ac.uk

Closing Date: 20^th September 2026 (midnight)

£50,733 to £59,484 per annum