Computational Chemistry Intern
Posted 19 Mar 2026
Advertised as “Computational Chemistry Intern (Materials Modeling/Molecular Simulation)”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- Remote
- Contract
- Internship
No salary listed for this role.
Field of studyChemical Engineering, Computational Chemistry, Materials Science, Physical Chemistry (or related)
Study levelDoctorate
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