TUHH - Hamburg University of Technology
Hamburg, Germany
You will explore and advance your understanding of the electrochemical and interfacial phenomena present in the nanoporous materials studied in BlueMat. The overarching goal is to develop and apply novel computational methods that bridge multiple scales, from nanoscopic to macroscopic, in collaboration with your peers. 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. This position will contribute fundamental insights into electrochemical processes and the dielectric behavior of confined water. These insights will support multiscale modeling efforts in electrochemistry and materials science.
YOUR CONTRIBUTIONS
* Develop hybrid machine-learned force fields (MLFFs) combined with polarizable force fields to enable accurate, computationally efficient electrochemical simulations
* Implement advanced charge equilibration (QEq) methods to incorporate long-range electrostatic effects and adapt atomic partial charges dynamically based on local chemical environments
* 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
YOUR PROFILE
Essential qualification
* Completed scientific university studies in Computational Science with a natural sciences specialization or in Physics, Chemistry or Materials Science with a specialization in scientific computing
Required knowledge and personal skills
* Curiosity and teamwork skills for working in interdisciplinary teams
* 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
Desired knowledge and personal 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
* Familiarity with object-oriented programming and parallelization methods, such as MPI or CUDA