This is an opportunity to undertake one of our new and exciting cross-disciplinary projects lying at the interface between computer science / mathematics and materials chemistry. The candidate does not need to have any knowledge in chemistry, but will need strong mathematical knowledge through a degree in maths, computer science, physics or engineering, as well as good programming skills. We have a number of different problems to be investigated and the projects intend to develop both new models & theories and also practical applications. The broader research areas to be employed include mathematical modelling & optimisation, machine learning & data analytics, as well as algorithms and statistical analysis methods. Examples of such techniques include combinatorial and constrained optimisation, neural networks, deep and reinforcement learning, statistical, unsupervised and supervised machine learning, signal/image processing, large-scale data visualisation, object sequencing, graph-based methods, computational geometry, and other methods. The student will work closely with our very strong teams of computer scientists, mathematicians, inorganic chemists, physicists and material scientists to develop ways of predicting and analysing new materials. The student will work in close collaboration with Johnson Matthey (FTSE 100) and apply developed techniques to the problems in next generation manufacturing and as well as in design of new catalysts to remove harmful species from car exhaust. The supervisory team has a strong track record in the defining ingredients of the underlying work and will closely contribute to the originality of the research. Supervision is provided from both Computer Science and Chemistry departments to appropriately support the discipline background of the student. Publications in top-tier theoretical and also application-oriented venues will be expected. These 42 month PhD projects will tackle multidisciplinary problems co-defined by our industrial partners working with the University of Liverpool. Core training in robotics, automation, data science, etc., will form part of a unifying curriculum, together with leadership and entrepreneurship training, to underpin the individual research projects.