Job opening: Marie Curie PhD fellowship – Machine Learning for Organic Electrolytes
We are looking for a PhD student who will research and implement new combined machine learning (ML) + molecular dynamics (MD) methods for quickly assessing the performance of redox-flow batteries based on organic electrolytes.
The work will build upon and improve SCM’s existing methods for computational chemistry, which include an advanced molecular dynamics driver and automated ML force field training. The focus of the project lies in the development of methods and software to help battery modelers worldwide more easily develop next-generation batteries.
In particular, one challenge to address includes accurately modelling long-range electrostatic effects, in order to be able to obtain atomistic insights into the structures – and the chemical reactions which drive structural changes – in the electrode, electrolyte and at the electrode/electrolyte interface.
The position is open to
- ML MSc graduates with an interest in chemistry/physics, and
- Theoretical chemistry/physics MSc graduates with an interest in ML.
