Job announcements relevant to people interested in electronic structure calculations…
Due to the large number of posts recently, there is currently a delay of several weeks between posts being submitted and the corresponding emails being distributed to all users. Please bear with us while we try to improve this. In the meantime – and until this notice is removed – it would assist us considerably if you could submit only important and/or urgent posts and thus help to reduce the size of the mail queue. Under no circumstances should you resend posts multiple times when you find the emails are not distributed immediately.
In light of the Russian military offensive in Ukraine, we request that announcements relating to events, jobs and other activities associated with institutions supported by the Russian and Belarusian states are not posted to the Psi-k forum.
Postdoc Machine Learning Potentials, Research Al ... (No replies)
Back to Job listings...
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 situated very close to the RUB-Campus in Bochum. We offer an exciting international research environment and excellent working conditions with state-of-the-art equipment and excellent 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 are the available topics?
We offer a broad spectrum of topics, from methodical developments in the field of machine learning potentials and the implementation of new methods in our software package RuNNer to more applied projects with the primary goal to understand in detail specific materials and reactions.
Application:
The successful candidate should have a PhD in chemistry, physics, materials science or a related field and very good communication skills in English (equal to B2 or better, 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]).