Their goal is to enable deeper understanding of thermodynamic and transport phenomena in electrochemical systems on atomic level using both quantum and classical simulations. Strong focus is placed on development and application of computational and data science methods for understanding and automated discovery of next-generation materials, primarily for energy storage and conversion.
- Development and application of atomistic methods for understanding and designing thermodynamic and transport properties of materials in electrochemical systems
- Computational discovery and characterization of next-generation materials systems
- Application of machine learning / data science methods to materials screening
- Establishing and directing projects within a global network of research collaborators
- PhD from a top university in physics, chemistry, chemical engineering, materials science, or a related field. Proven research track record and a strong foundation in thermodynamics, chemical kinetics, transport phenomena and electronic structure.
- Background in quantum chemistry or first-principles computations of materials properties, experience with electronic structure methods and molecular dynamics
- Fluency in Python, experience with Fortran or C++ preferred.
Read the detailed job opening announcement here.
30 November 2017
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