SES - [US] Computational Chemistry Intern (Materials Modeling/Molecular Simulation)
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 • Language Requirement • Professional English proficiency is required, including technical discussions, documentation, and presentations
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
Benefits
• 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
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