A post doc position is available in the theoretical surface science group at Department of Physics and Astronomy at Aarhus University. The successful candidate will introduce and optimize the use of machine learning techniques in connection with computational modeling of the atomic structure of surfaces of crystals and nanoparticles. The surface science group has considerable experience using evolutionary algorithms and is now seeking to extend these competences further by introducing other machine learning techniques such as supervised learning. The ultimate aim is to construct strategies for computer codes that can identify the thermodynamically most stable structures of crystals and nanoparticles under given environmental conditions. Such structures will be compared with experimental information and be used as templates for simulation of chemical reactions of interest in the field of heterogeneous catalysis. The successful candidate must document experience at the highest level within machine learning and/or density functional theory of surface science and catalysis.

Applicants for the post doc position must have a Ph.D. in physics, chemistry, nanoscience, or computer science. The starting date is expected to be early 2017, but the exact date is negotiable. Funding is available for 1 years of employment. In case further external funding is secured within the academic area the candidate is assessed obviously qualified for, an extension is possible.

Place of work and area of employment
The place of work is Ny Munkegade 120, DK-8000 Aarhus, and the area of employment is Aarhus University with related departments.

Questions regarding this job offer should be directed to Professor Bjørk Hammer [email protected]. 

Deadline

All applications must be made online and received by:

15.01.2017

Please apply online here