[Japan] 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: Japan (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