Computational Chemistry Intern – Materials Modeling, Molecular Simulation

Job not on LinkedIn

🔥 18 hours ago

🇨🇳 China – Remote

👨‍🎓 Internship

⚪️ Entry-level

👻 Ghost score 12%

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SES AI Corp

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2021

🚘 Automotive

🏭 Manufacturing

🚗 Transport

Automotive • Manufacturing • Transport

SES AI Corp is a company at the forefront of battery innovation, specifically focusing on the development of next-generation lithium-metal (Li-Metal) batteries for electric transportation vehicles such as EVs (electric vehicles), drones, and urban air mobility devices (UAMs). Their technology aims to enhance battery safety, range, and cost-effectiveness. Furthermore, SES AI leverages artificial intelligence to accelerate material discovery, improve manufacturing quality control, and monitor battery health. The company collaborates with major technology firms and has successfully tested its batteries to meet global safety standards, illustrating its commitment to advancing electric transportation both on land and in the air.

📋 Description

• Support computational chemistry modeling and simulation of advanced electrolyte systems for the SES Molecular Universe project • Perform molecular dynamics simulations for liquid-phase systems, 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 structural properties such as radial distribution functions, coordination numbers, and solvation structures • Analyze dynamic properties such as diffusion coefficients and ion transport behavior • Extract thermodynamic and statistical properties • Build and improve automated data-processing pipelines for simulation efficiency, reproducibility, and scalability • Convert simulation outputs into reports, visualizations, and presentations supporting scientific and engineering decision-making • Collaborate with internal teams to improve workflow robustness and reproducibility across simulation pipelines • Support scaling and engineering of molecular simulation workflows within the Molecular Universe platform • Potentially contribute to force field development, optimization, and validation • Explore higher-accuracy or higher-efficiency simulation methodologies • Participate in workflow automation, orchestration, and task scheduling • Collaborate across global teams

🎯 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 • 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

🏖️ 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 • Training and mentorship from a computational scientist

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