[South Korea] Computational Chemistry Intern (Materials Modeling/Molecular Simulation)

**Computational Chemistry Intern (Materials Modeling / Molecular Simulation)** **About Us** SES AI is a leader in AI-driven materials discovery, building the **Molecular Universe (MU)** platform to accelerate the development of next-generation battery chemistries. Our work integrates physics-based simulations, machine learning, and large-scale data infrastructure to enable rapid innovation in material science with a dedication to AI for Science. To learn more about SES, please visit: www.ses.ai **Position Scope** SES AI is seeking a Computational Chemistry Interns to join the Molecular Universe team and support computational modeling and simulation of advanced electrolyte systems. This is a hands-on research role focused on liquid-phase molecular dynamics (MD) simulations, especially for electrolyte systems relevant to next-generation batteries. Interns will receive training and mentorship from our computational scientist, and collaborate across global teams. * ****Location:**** South Korea (Remote) * ****Duration:**** 6 months **Responsibilities** * Contribute to the SES Molecular Universe project by supporting computational chemistry modeling and simulation of advanced electrolyte systems * Independently or collaboratively perform molecular dynamics simulations for liquid-phase systems, especially electrolytes, including system construction, initial structure generation, and simulation parameter setup * Execute the full MD workflow, including job submission, HPC resource utilization, run monitoring, troubleshooting, and issue resolution * Analyze simulation results in depth, including but not limited to: * Structural properties such as radial distribution functions (RDF), coordination numbers, and solvation structures * Dynamic properties such as diffusion coefficients and ion transport behavior * Thermodynamic and statistical property extraction * Build and improve automated data-processing pipelines to enhance simulation efficiency, reproducibility, and scalability * Convert simulation outputs into clear reports, visualizations, and presentations that support scientific and engineering decision-making * Collaborate with internal teams to improve workflow robustness and reproducibility across simulation pipelines * Support the scaling and engineering of molecular simulation workflows within the MU platform **Preferred / Advanced Responsibilities** * Contribute to force field development, optimization, and validation for electrolyte or ion-containing systems * Explore higher-accuracy or higher-efficiency simulation methodologies * Participate in the engineering and platformization of simulation workflows, including workflow automation, orchestration, and task scheduling **Qualifications** * PhD (or PhD candidate) in Computational Chemistry, Materials Science, Chemical Engineering, Physical Chemistry, or a related field * Hands-on experience with molecular dynamics simulations, particularly for liquid-phase systems * Familiarity with common simulation tools such as GROMACS, LAMMPS, OPENMM, or similar packages * Experience with electrolyte systems, ionic systems, battery-related simulations, or sodium-ion systems is strongly preferred * Understanding of molecular force fields, including basic principles of force field development and parameterization; direct experience is preferred * Programming skills in Python or similar languages for data analysis, workflow automation, and simulation pipeline development * Strong problem-solving skills and the ability to diagnose simulation instability, convergence issues, and physical inconsistencies * Excellent communication skills, with the ability to clearly present technical findings to both technical and non-technical audiences * Ability to work effectively in a collaborative, international research environment **Language Requirement** * Professional English proficiency is required * For positions based in Korea, Japan, and Mainland China, candidates must speak English fluently and be able to conduct professional work in English, including technical discussions, documentation, and presentations **Why Join SES AI** * Work on real, high-impact problems in next-generation battery materials discovery * Contribute to production-relevant simulation workflows rather than isolated academic projects * Gain exposure to the intersection of molecular simulation, automation, AI for Science, and materials innovation * Collaborate with a global team across simulation, machine learning, and experimental validation