Postdoc Position in Machine Learning Potentials, Research Alliance Ruhr, Germany

A Postdoc position with a focus on the development and application of neural network potentials is immediately available at the Chair for Theoretical Chemistry II of Prof. Dr. Jörg Behler at Ruhr-Universität Bochum (initial funding for 2 years with possible extension, payment according to public service’s salary agreement: TVL E13, 100%). The position will be filled as soon as possible or on mutual agreement at a later stage.

Who we are:

As Chair for Theoretical Chemistry II of Ruhr-Universität Bochum we are part of the newly founded Research Center Chemical Sciences and Sustainability of the Research Alliance Ruhr (RAR). The RAR is a joint initiative of TU Dortmund, Ruhr-Universität Bochum and Universität Duisburg-Essen funded by the state of North Rhine-Westphalia to further expand the research landscape in the metropolitan area Ruhr. Our group is located next to the RUB-Campus in Bochum. We offer an exciting international research environment and excellent working conditions with state-of-the-art equipment and high-performance computing facilities.

What do we do?

As a very interdisciplinary team we are working at the frontier between chemistry, physics and materials science performing atomistic simulations of complex systems. Our central interests are chemical processes at interfaces (e.g. electrochemistry and catalysis) and reactions in solution. To study these topics, we develop modern machine learning potentials, which combine the accuracy of quantum mechanical electronic structure methods and the efficiency of classical force fields. With these tools we can perform molecular dynamics simulations of large systems on long time scales to understand in detail mechanisms and properties at the atomic scale. Our group is among the world-leading groups in the field of machine learning potentials, and in the past two decades we have substantially contributed to the progress in this field.

What is the topic of the position?

We offer a broad spectrum of topics, from methodical developments in the field of machine learning potentials in our software package RuNNer to applications of machine learning potentials. A particular focus of this project is the investigation of complex solid-liquid interfaces by molecular dynamics simulations in collaboration with experimental groups.

Application:

The successful candidate should have a PhD in chemistry, physics, materials science or a related field and very good communication skills in English (oral and writing). The ideal candidate will have some knowledge about electronic structure calculations (preferably density functional theory), atomistic simulation techniques like molecular dynamics or Monte Carlo, and programming skills in Python and/or Fortran.

Interested candidates should send their detailed application documents (motivation letter, CV, copies of certificates and transcripts, list of publications) as a single pdf-file by e-mail to [email protected]. The position will be filled as soon as a suitable candidate has been found.

Questions?
In case of any questions you are welcome to contact us anytime ([email protected]).