AddressThe Frontier of Atomistic Simulation Techniques (FAST) group at the Cavendish Laboratory, University of Cambridge aims to fill a Postdoctoral Research Assistant/Associate position, for 2 years with the possibility of extension, subject to funding. This position is intended as a group position in a research programme on 'Atomistic insight into liquid-phase heterogeneous catalysis' involving Assistant Professor Christoph Schran, who leads the FAST group.
We are particularly interested in the application and methodological development of machine learning techniques to combine electronic structure and modern sampling techniques in liquid-phase heterogeneous catalysis. Experience in some, but not all, of these areas is expected from the successful candidate. In close collaboration with the members of the group and other groups in Cambridge, the role holder will push forward the methodological description of reactions at solid/liquid interfaces and apply these techniques to reactions of high societal relevance, such as CO2 reduction or water splitting.
As part of the Lennard-Jones Centre, which brings together the materials and molecular modelling community in Cambridge, the role holder will be in direct exchange with other leading groups in the field. Furthermore, embedded in the Theory of Condensed Matter group, the role holder will overall be connected to a lively and stimulating scientific and social environment, with regular activities ranging from internal and external group seminars, graduate lectures, and introductory presentations on topics of current research.
The role holder will be expected to conduct postdoctoral-level research in the FAST group. Collaboration with other members of the group, in particular other postdocs and junior members, as well as collaborations across and outside the department are highly encouraged. The role holder will also be expected to contribute to the general life and scientific environment of the group, e.g., contributing to the organisation of internal and external seminars, and offering graduate-level lectures based on their current topic of research.
The successful candidate will hold (or be close to obtaining) a PhD in Physics, Theoretical or Computational Chemistry, Materials Modelling (or closely related disciplines), with a proven track record in conducting and publishing scientific research in modern materials and molecular modelling. Preference will be given to candidates with experience in using machine learning to combine electronic structure and sampling techniques in the study of complex materials, as well as candidates with experience in computational heterogeneous catalysis. The candidate will also have the ability to manage workload, engage in collaborations and communicate results in talks and presentations.
