Overview
This PhD will develop fundamental AI methods that help chemists explore and navigate complex chemical spaces when data are scarce, high-dimensional, and continuously updated by new experiments. The core aim is to design representation learning approaches that can extract chemically meaningful structure from limited experimental labels, update as new measurements arrive, and remain aligned with how chemists actually make decisions.
About this opportunity
A central challenge is that many chemical discovery problems have only a small number of experimental measurements, yet the underlying molecular spaces are vast and complex. Dimensionality reduction and representation learning can reveal hidden structure, but naïve compression risks discarding crucial chemical information and producing misleading insights. This project will address that by creating modelling strategies that prioritise what matters for downstream chemical objectives under sparse supervision.
You will develop AI modelling and analysis pipelines that:
- learn task-relevant molecular representations from limited experimental measurements,
- support incremental/online learning as new data arrives,
- incorporate human-in-the-loop guidance so domain experts can steer which patterns should be preserved for the chemical task,
- and evaluate performance in realistic chemical discovery workflows with close collaboration with chemists.
Training environment
This is a joint collaboration between two centres:
- Centre for AI Fundamentals, University of Manchester
- AI Hub in Chemistry (AIchemy), University of Liverpool
You will be supported by an interdisciplinary supervisory team spanning fundamental AI research in representation learning (Wood, D., et al., JMLR, 24(359):1−49, 2023) and human-in-the-loop discovery (Nahal, Y., et al., J Cheminform 16, 138 (2024)), as well as applied AI (Cissé, A., et al., IJCAI, 2025) and automation (Dai, T., et al., Nature 635, 890–897 (2024)) for chemistry. You will work across both sites and communities, but be primarily based at the University of Liverpool, with monthly in-person visits to the Centre for AI Fundamentals (University of Manchester) to engage with the broader research community and meet with the joint supervisory team.