Job Opportunities
Current Opportunities
A PostDoc position on a very exciting project co-directed by Mathieu Salanne and Alessandra Serva is now open.
Current batteries suffer from a trade off between high power and high energy density In the context of a bilateral French/Germany project, we aim to develop high power batteries ( Na based), which would enable fast charging and a long life, and at the same time guarantee sufficient energy density. To do so, we need to understand the underlying physicochemistry of redox reactions and mass transfer phenomena at the microscopic scale for these systems. Molecular modelling allows to reach this level of detail. Machine learning force fields[1] are now recognized as a good compromise between accuracy and simulation cost, allowing to model redox reactivity and having large simulation cells at the same time.
In this project, the postdoctoral researcher will develop a machine learning augmented potential for high power batteries electrolytes selected in agreement with our experimental collaborators. Machine learning potentials can achieve ab initio quality at short distances, but incorporating long range physics is a more difficult task. Classical force fields can model long range interactions with reasonable accuracy. Thus, we plan to use on the fly uncertainty estimation techniques and learn the correction between a baseline classical force field and ab initio level energies and forces.
The postdoctoral researcher recruited will work as part of a team of researchers in the PHENIX laboratory at Sorbonne University, as part of the HIPOBAT project funded by PEPR and BMBF research programs. He or she will also interact with other doctoral and postdoctoral researchers recruited within the HIPOBAT project.
[1] X Lian, M Salanne J Chem Phys 159 144705 2023
Candidate profile: We are looking for a motivated candidate with strong background in chemical physics, statistical mechanics and skills in programming, molecular modelling (molecular dynamics) and machine learning applied to physical chemistry problems. Experience in force field developments is a plus. A good level of English, spoken and written, is also required, as well as the capability of working both as part of a team as individually.
How to apply: To apply, please send us a CV, a short letter explaining why you would like to join us, and the contact details of one or two reference persons whom you have worked with that we can contact.
Duration: 2 years. The position will take place in the PHENIX laboratory located on the Pierre et Marie Curie Campus of Sorbonne Université.
Contact: mathieu.salanne@sorbonne-universite.fr, alessandra.serva@sorbonne-universite.fr and rocio.semino@sorbonne-universite.fr
There are no current PhD or PostDoc vacant positions within the MAGNIFY project, but I am always happy to discuss opportunities with motivated students or researchers with experience in atomistic classical or reactive force-field based molecular modelling, multi-scale modelling, enhanced sampling techniques and/or machine-learning applied to data science.
MORE INFORMATION ON THE MAGNIFY PROJECT: click here