Inorganic/Organic interfaces play a crucial role for the performance of organic electronic devices, such as OLED-TVs and photovoltaic cell. Of particular interest is the interface geometry, since the structure determines most, if not all, of its properties. However, determining the structure experimentally often proves to be difficult due to the small amount of material that is used. There is, therefore, a strong need of theoretical support. To provide this support, my group and I have recently developed a Machine Learning based algorithm that allows determining the interface structure with a higher precision, and at a lower cost, than any other existing approach.
The aim of this PhD thesis is to act as link between theory development and our experimental collaboration partners and apply our recently developed method to a diverse range of systems of practical interest. These include, for example, carbon monoxide at Ga-doped zinc oxide (Christof Wöll, Karlsruhe Institute of Technology), small molecules at metal surfaces (Daniel Wegner, Radboud University), or self-assembled monolayers at insulator films (Philipp Rahe, University of Osnabrück).
> A friendly group with a productive atmosphere
> Training in skills relevant for industry, including machine learning, computational material science, and organic electronics
> Visiting multiple (international) conferences per year
> Collaboration with theoretical and experimental groups at the TU Munich
> An extended abroad stay at a renowned international research facility
We seek: Highly motivated, self-propelled students with an interest in solid state physics and computational material science.
Compensation: € 2.112,40 per month, 14x per year (funded by the START-prize)
Oliver Hofmann email: email@example.com
Tel: 0316 873 8964 http://www.if.tugraz.at/hofmann
Or talk to the students in office PH 02 152 (2nd floor, right by the stairs)