Your key responsibilities will include integrating advanced charge equilibration (QEq) methods into machine-learned force fields (MLFFs) to achieve ab initio-level accuracy at a reduced computational cost and modeling long-range electrostatic effects, including Faradaic processes, in electrochemical environments. These insights will support multiscale modeling efforts in electrochemistry and materials science.
Develop hybrid machine-learned force fields (MLFFs) combined with polarizable force fields to enable accurate, computationally efficient electrochemical simulations
Conduct high-fidelity molecular dynamics simulations informed by DFT and MLFFs to investigate the atomistic mechanisms of water splitting, diffusion in confinement, and transport properties at electrochemical interfaces
Collaborate closely with BlueMat partners in imaging, modeling, and fluid transport to integrate your findings into the broader cluster program
Share your findings through publications and engage with the research community and the public
Very good English required (at least B2/C1 level according to CEFR) – German is not mandatory
Substantial knowledge of programming languages, including C++ and Python and its application to solve natural science problems
Experienced in high-performance computing and software development with strong programming skills
Proficient in using common atomistic simulation software for performing molecular dynamics or ab-initio calculations; experience with machine learning-based force fields is advantageous