AI Safety research experience and scholarship

The University of Birmingham is at the forefront of developing technology for technical AI Safety. This summer, we offer a unique opportunity for taught students to study alongside leading researchers in AI Safety as part of a collaborative initiative with the University of Manchester, funded by the Advanced Research and Invention Agency (ARIA).

This project offers a generous scholarship worth up to £5000, enabling participants to fully engage in an intensive research-based learning experience in AI Safety. Successful scholarship awardees will pay no programme fees and receive free accommodation at the University of Birmingham halls of residence.

Learn more about the scholarship and how to apply.

Project 1

Automated Manufacturing Design

Research team

Mirco Giacobbe, Leonardo Stella, Adam Szekely, Gokhan Tut

Research objective

Predictive modelling of biopharmaceutical stability remains one of the most significant bottlenecks in modern drug development. While traditional machine learning methods offer speed and flexibility, they often lack the physical grounding required to satisfy rigorous regulatory standards for shelf-life estimation.

In this project, you will explore the development of a framework for assessing biologic degradation by bridging the gap between high-dimensional structural data and fundamental chemical kinetics. The goal is to understand whether a physics-informed modelling framework can provide the level of safety, reliability, and interpretability required to support critical decision-making within the strict regulatory environment of biopharmaceutical manufacturing.

Project 2

Hardware-level Verification

Research team

Mirco Giacobbe, Edwin Hamel-de le Court, Edoardo Manino

Research objective

Hardware-level considerations are often overlooked when evaluating the safety of AI models. Yet details such as quantisation, sampling, and software implementation can substantially alter model behaviour during deployment. AI models, including neural networks, are not immune to these effects.

In this project, you will study research at the intersection of the digital and physical worlds while investigating several key questions: Do AI deployment pipelines truly preserve the intended behaviour of models? Are AI-based digital controllers robust and stable when interacting with a physical environment? Can we formally and automatically verify that AI decision-making systems remain safe when implemented on real hardware?

Project 3

Privacy-preserving Verification

Research team

Pascal Berrang, Mirco Giacobbe, Xiao Yang

Research objective

Formal verification of systems has focused for decades on the challenge of scalability. However, ensuring AI Safety at a global scale introduces an additional challenge–confidentiality. Existing verification technologies typically require access to a formal and detailed description of the system, which can create tension between providers’ desire to protect their intellectual property and regulators’ need to ensure safety.

This project investigates how one can convince an external verifier that a system is safe without revealing the system itself. Participants will explore novel questions at the intersection of formal verification, cryptography, and AI, studying new approaches to safety assurance in settings where confidentiality is essential.

Project 4

Formal Certification for AI Safety

Research team

Mrudula Balachander, Mirco Giacobbe, Grigory Neustroev, Diptarko Roy

Research objective

Formally verifying an AI system involves determining whether it satisfies a precise specification of its intended behaviour. Standard testing techniques are easy to implement but inherently non-exhaustive and cannot provide formal guarantees of safety, whether absolute or probabilistic.

In this project, participants will learn advanced techniques at the intersection of logic and AI while working with a team investigating formal specification languages for AI Safety and algorithms for verifying them. A central goal is to understand what constitutes a formal certificate of safety and how learning systems might eventually be enabled to generate proofs of their own safety.