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Computational Chemistry Intern
Location
United States
Posted
81 days ago
Salary
0
Seniority
Entry Level
No structured requirement data.
Job Description
Computational Chemistry Intern
SES
Role Description SES AI is seeking a Computational Chemistry Intern 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: U.S. Pacific Time Zone (Remote) - Candidate must be based in the U.S. West Coast region to support business operations. 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, 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.
Job 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.
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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