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Contemporary Amperex Technology · 香港, 中国

# MD Scientist I MD分子动力学科学家

香港, 中国 Research & Science 

Listed on Contemporary Amperex Technology’s official careers site. Listed as of October 19, 2026. Check that it is still open and apply there.
[Apply on Contemporary Amperex Technology’s careers site →](https://app.mokahr.com/social-recruitment/catlhr/98098#/job/01401364-733e-434b-9301-e3df9824d7f6)

[OpenNash Jobs](https://jobs.opennash.com/) / [Contemporary Amperex Technology](https://jobs.opennash.com/companies/contemporary-amperex-technology/) / MD Scientist I MD分子动力学科学家

## Job description

### Responsibilities / 工作职责

Develop and optimize MD workflows for electrolytes, interfaces, and polymer systems.搭建并优化电解液、界面与聚合物体系的分子动力学（MD）模拟流程。 Build top-down fitting pipelines to calibrate force-field/CG parameters against target observables.开发 top-down fitting 流程，基于目标指标对力场/粗粒化参数进行拟合与迭代优化。 Develop and integrate machine-learning force fields / ML interatomic potentials when appropriate.开发并在需要时集成机器学习势（ML force field / ML interatomic potential）到模拟工作流中。

Ensure modular, efficient, reproducible code and maintain clear documentation.保证代码模块化、高效、可复现，并维护清晰文档。

### Qualifications / 任职要求
- PhD or equivalent or related working experience in Materials/ Chemistry/Physics/ Biology/Engineering/CS/AI or related fields.材料/化学/物理/生物/工程/计算机/AI 等相关方向博士或同等相关领域工作经验。 Hands-on experience with MD (e.g., GROMACS/LAMMPS/OpenMM) and trajectory analysis.具备 MD 实操经验（如 GROMACS/LAMMPS/OpenMM）及轨迹分析能力。 Strong Python skills; experience with ML frameworks is a plus.Python 编程能力强；有机器学习框架经验者加分。 Experience with ML force fields / GNN/equivariant models is preferred.有机器学习势或 GNN/等变模型经验者优先。 Good professional English communication and strong problem-solving skills.具备良好的英文沟通能力与问题解决能力。

## At a glance
- Posted by the employer: Not stated
- First seen by OpenNash: Oct 19, 2026
- Last checked: Oct 19, 2026
- Listed for: Under a day
- Location: 香港, 中国[Apply on Contemporary Amperex Technology’s careers site →](https://app.mokahr.com/social-recruitment/catlhr/98098#/job/01401364-733e-434b-9301-e3df9824d7f6)

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