# Computational Chemistry Intern (Materials Modeling/Molecular Simulation) at SES

Computational Chemistry Intern (Materials Modeling/Molecular Simulation) at SES in China. Remote, internship. Posted March 19, 2026.

- Company: SES
- Location: China
- Workplace: Remote
- Job type: Internship
- Posted: 2026-03-19
- Apply: https://boards.greenhouse.io/sesai/jobs/4675167005?gh_jid=4675167005
- Source: https://jobmagic.ai/jobs/computational-chemistry-intern-materials-modelingmolecular-simulation-ses-1d0e9t

## Requirements

- 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

## Description

**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:** China (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

 
