Scientists have speculated about the inherent potential of molecular systems for information processing since the dawn of computer science. However, the digital computing paradigm is inherently ill-suited for molecular system. Recently, we reported a major breakthrough in using chemical reaction networks for so-called in chemico reservoir computing (Nature2024, 631, 549–555). This work demonstrated that self-organising reaction networks and the emergent complexity in such systems form powerful reservoir computers capable of non-linear classification, times-series prediction and forecasting, on a par or even outcompeting in silico algorithms.

The ambition of this project is to expand in chemico reservoir computing to other reaction networks, especially enzymatic reaction networks. By incorporating light-sensitive groups and exploiting the inherent temperature and pH sensitivity of enzymes, we will construct chemical computers that sense their physico-chemical environment and process this information into desired outputs. Ultimately, we wish to use these chemical reservoirs as a molecular ‘brain’ that can interface with both electronic and biological systems.

In this project, you will combine a deep knowledge of chemical reaction networks with robotic systems and analytical science. You will also learn how to programme robotic systems and how to implement aspects of deep learning and neural networks for reservoir computing. You will be part of the Big Chemistry consortium and will also be involved in training and teaching BSc and MSc students. We are looking for two PhD candidates for this opening.

Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about working as a PhD candidate