PhD in Computational Modelling at the Atomic Scale of Electrolyte Dynamics in Metal-air Batteries Sólo en [en]?

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Válido hasta 30.09.2019


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CIC energiGUNE has been awarded by the European Commission with the ´HR Excellence in Research´which reflects our commitment to achieve fair and transparent recruitment and appraisal procedures,and certifies the existence of a stimulating and favorable work environment for researchers in our institution

CIC energiGUNE  offers the opportunity to complete a PhD in the field of Computational Modelling at the Atomic Scale  of Electrolyte Dynamics in Metal-air Batteries. The offer is aimed at motivated students who have good academic records and are ready to carry out a challenging and exciting 3-year applied research project.

Qué ofrecemos

The project involves the development and application of density functional theory calculations and ReaxFF-based molecular dynamics simulations, which will enhance our atomistic understanding of non-aqueous liquid electrolytes of interest for next-generation battery technologies. The simulations will focus on ionic transport, electrolyte-electrode interactions, and aging mechanisms for different chemistries in metal-air battery systems. The project will be carried out in close collaboration with other CIC-based and international experimental partner laboratories and will aid the atomistic interpretation of novel solvation experiments and electrochemical measurements

Qué buscamos

We are searching for a highly motivated and independent researcher with a Master degree in Physics, Chemistry, Materials Science or other related topics. Students who expect to obtain their Master degree before September 2019 can also apply.

Good knowledge in quantum mechanics and statistical mechanics is expected and interest in writing computer code (e.g., Python) and shell scripts is an asset.

Experience with interatomic potentials for molecular dynamics simulations is desirable, but not essential-

Experience with density functional theory methods, such as electronic structure calculations using VASP, Quantum Espresso, FHI-aims and/or similar packages is an advantage.

Basic experience with machine learning algorithms and/or network science is an advantage.

The candidate should also be able to work independently and as part of a highly ambitious research team, as well as have very good command of English..

He/she should be a good team player who can collaborate with other scientists. Highly motivated person an interested in investigation. She/he will be incorporated to a multidisciplinary team.


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