[Japan] Computational Chemistry Intern (Materials… at SES AI · Remote

Summary: SES AI is a leader in AI-driven materials discovery, building the Molecular Universe platform to accelerate the development of next-generation battery chemistries. The company is seeking a Computational Chemistry Intern to support computational modeling and simulation of advanced electrolyte systems through hands-on research focused on liquid-phase molecular dynamics simulations. 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 - 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 Required 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 - 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 - Professional English proficiency is required Preferred Qualifications: - Experience with electrolyte systems, ionic systems, battery-related simulations, or sodium-ion systems is strongly preferred - 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 Required Skills: Molecular dynamics simulations, Liquid-phase molecular dynamics, Electrolyte systems, GROMACS, LAMMPS, OPENMM, Molecular force fields, Force field development, Force field parameterization, Python programming, Simulation workflow automation, High-performance computing (HPC) resource utilization, Data analysis for molecular simulations