Battery AI & Digital Twin Engineer (Cell Design & Simulation)
Job Description
Ampace (新能安) is a global battery-technology company specializing in lithium-ion cells and systems for energy storage, data centers, and critical power. Our R&D pairs advanced cell chemistry with semi-solid-state technology to deliver safe, high-performance batteries at scale. This position is based at our R&D center in Xiamen, China.
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Responsibilities:
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1. Develop hybrid physics-ML models that predict and optimize cell energy density, cycle life, and thermal behavior.
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2. Map core design parameters — coating loading, calendering pressure, electrode porosity — to electrochemical fields such as liquid-phase diffusion, solid-phase overpotential, and heat flux.
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3. Extend electrochemical mechanistic models and fuse them with machine learning (physics-informed neural networks, neural operators) into fast multiphysics surrogate models.
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4. Lead inverse-design using active learning and Bayesian optimization to cut physical trial-and-error and the number of DOE runs.
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5. Design and maintain versioned, high-concurrency simulation data pipelines and an MLOps workflow.
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Requirements:
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1. PhD in Computer Science, Applied Mathematics, Physics, Mechanical Engineering, or a related quantitative or computational field; strong Python and industrial-grade ML/DL project experience on physical or engineering data.
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2. Electrochemical mechanistic modeling (SPM, P2D/DFN) or thermal simulation; PyBaMM, COMSOL, or ANSYS a plus; deep understanding of energy-density calculation and aging mechanisms such as lithium plating and SEI growth.
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3. Mechanism-ML hybrid methods (PINNs, neural operators, surrogate solvers); Bayesian inference, uncertainty quantification, Bayesian optimization, and active learning; MLOps tooling (DVC, LakeFS, MLflow, W&B) a plus.
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新能安（Ampace）是一家全球领先的电池科技企业，专注于锂离子电芯及电池系统的研发与制造，产品广泛应用于储能、数据中心及关键电源等领域。公司结合先进的电芯化学体系与半固态电池技术，致力于打造兼具高安全性、高性能和规模化生产能力的电池产品。
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本岗位工作地点位于中国厦门研发中心。
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岗位职责
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- 开发融合物理机理与机器学习的混合模型，用于预测和优化电芯能量密度、循环寿命及热性能。
- 建立涂布载量、辊压压力、电极孔隙率等核心设计参数与液相扩散、固相过电位、热流密度等电化学场之间的映射关系。
- 基于电化学机理模型进行扩展开发，并结合机器学习方法（如物理信息神经网络（PINNs）、神经算子等），构建高效的多物理场代理模型。
- 基于主动学习（Active Learning）和贝叶斯优化（Bayesian Optimization）开展电池反向设计，减少实验试错成本及DOE（实验设计）次数，提高研发效率。
- 设计并维护支持版本管理、高并发计算的仿真数据平台，建立并持续优化MLOps开发与部署流程。
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任职要求
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- 计算机科学、应用数学、物理学、机械工程或相关计算类专业博士学历；熟练掌握Python，具备工业级机器学习或深度学习项目开发经验，有物理或工程数据建模经验。
- 熟悉电化学机理建模（如SPM、P2D/DFN模型）或热仿真，具有PyBaMM、COMSOL、ANSYS等工具使用经验者优先；深入理解电池能量密度计算方法及锂析出（Lithium Plating）、SEI膜生长等老化机理。
- 熟悉机理模型与机器学习融合方法，如物理信息神经网络（PINNs）、神经算子（Neural Operators）、代理模型（Surrogate Model）等；具备贝叶斯推断、不确定性量化、贝叶斯优化、主动学习等相关经验；熟悉DVC、LakeFS、MLflow、Weights & Biases（W&B）等MLOps工具者优先。
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