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

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