We are seeking a highly motivated doctoral student to work in the field of thermal and electrochemical oxidation catalysis. The project focuses on investigating catalytically relevant metal oxide systems using fully atomistic machine learning molecular dynamics simulations in explicit solvation. You will calculate quantitative, time-resolved IR and Raman spectra of these systems, enabling direct comparison with experimental measurements. Your work will involve identifying spectral fingerprints of catalytic reactions under realistic conditions, leading to an in-depth atomistic understanding of reaction mechanisms at metal oxide interfaces.
To achieve this, you will develop and parameterize machine learning models for vibrational spectra calculations. This includes generating training data using electronic structure methods and atomistic simulations. The project also has a strong methodological component, including contributions to the development of our in-house code Mimyria (https://github.com/pschienbein/mimyria) for machine-learning-accelerated vibrational spectroscopy.
The successful candidate is expected to present results at international conferences, publish in peer-reviewed journals, and actively participate in national and international collaborations within the research group.
