From Single Robotic Chemist Action to End-to-End Experiment Resilience

About the Project

This PhD will develop fundamental robotic methods that would allow robotic chemists to reason through a series of learned skills and adapt the experimental workflow when experiments are interrupted due to adaptive chemical protocols, experimental and environmental changes and hardware failures. The core aim is to design a resilient robotic framework that can autonomously replan within real-world laboratory conditions towards adaptive experimentation and long-term robust experiments.

Current paradigms in robotic chemistry are largely constrained by deterministic, linear pipelines or isolated, single-task learned models. These architectures lack the cognitive flexibility required for deployment in non-deterministic, human-centric laboratory environments. This project will address that by developing novel methods that prioritise multi-step experimental resilience, ensuring the integrity of complex chemical protocols over extended operational periods.

You will develop robotics methods that:

• learn task-relevant manipulation skills towards building long-horizon experiments
• support autonomous replanning in the presence of unexpected disturbances
• incorporate an experiment-aware agentic framework that can steer the robotic system towards completing the chemistry workflow,
• evaluate performance in realistic chemical discovery workflows with close collaboration with chemists.

Training and Collaboration

This is a joint collaboration between two AI and robotics centres:

• Centre for AI in Assistive Autonomy, University of Edinburgh
• AI Hub in Chemistry (AIchemy), University of Liverpool

You will be supported by an interdisciplinary supervisory team spanning robotics, AI, chemistry automation and materials chemistry. You will work across both sites and communities, but be primarily based at the University of Liverpool, with secondments in-person to the Centre for AI in Assistive Autonomy (University of Edinburgh) to engage with the broader research community and collaborate closely with the joint supervisory team.

We welcome applicants with a strong background and a Master’s degree in one or more of:

• Robotics, Machine Learning / Data Science / Computer Science / Applied mathematics
• Mechatronics / Engineering/ related quantitative disciplines

Essential:

• Strong robotics, AI and math background
• Evidence of Python programming experience

Desirable:

• Enthusiasm for interdisciplinary research
• Interest in collaborating with experimental scientists
• Hands-on experience with real robotic systems

Please submit:

1. CV
2. Brief cover letter outlining your interest and relevant experience

to Dr Gabriella Pizzuto - Gabriella.Pizzuto@liverpool.ac.uk

Funding Notes

UK Tuition fees, stipend funded for 3.5 years. Funded from AI for Chemistry Hub based at University of Liverpool. International students are welcome to apply but international tuition fees need to be paid for