**Job Description**
This 3-year PhD scholarship at DTU Energy focuses on integrating innovative physics-based atomistic modeling methods with data-driven inverse molecular design to develop highly stable organic flow battery electrolytes. The computational work will involve close collaboration with experimental counterparts, specifically targeting the prediction of degradation mechanisms and the elucidation of structure-stability relationships. The ultimate goal is to enable the inverse design of improved electrolyte materials using machine-learning models.
**Skills & Abilities**
• Demonstrated aptitude for computational chemistry or related research.
• Experience with machine-learning applications and/or modelling of chemical reactions (additional asset).
• Working knowledge of computational chemistry at the MSc level.
• Working knowledge of a modern computer programming language.
**Qualifications**
Required Degree(s) in:
• Computational Chemistry or related field
**Experience**
Other:
• Demonstrated aptitude for computational chemistry or related research.
• Working knowledge of computational chemistry at the MSc level and of a modern computer programming language.
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