Control and inversion of ultrafast imaging by machine learning

X-ray Free-Electron Lasers (XFELs) enable completely new types of measurements capable of characterising chemical dynamics and light-induced processes in unprecedented detail. However, it is clear that the analysis of the large and complex data sets is an overlooked bottleneck. We have begun to investigate machine learning techniques for analysis and inversion of data, potentially providing real-time feedback and control of experiments. The project will develop new algorithms (based on machine learning and otherwise) to invert ultrafast scattering data into a physical information about molecules.
We are searching for a talented and ambitious PhD candidate to join our international team. A desire to learn more about fundamental aspects of photochemistry, chemical dynamics, and to work on theory and simulations in close conjunction with experiments is crucial. The project is run in collaboration with the group of Professor Subramanian Ramamoorthy (School of Informatics).