Battery AI & Digital Twin Engineer (Cell Design & Simulation)

Ampace (???) 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.

Before applying for this role, please read the following information about this opportunity found below.

Responsibilities:

1. Develop hybrid physics-ML models that predict and optimize cell energy density, cycle life, and thermal behavior.

1. Map core design parameters — coating loading, calendering pressure, electrode porosity — to electrochemical fields such as liquid-phase diffusion, solid-phase overpotential, and heat flux.

1. Extend electrochemical mechanistic models and fuse them with machine learning (physics-informed neural networks, neural operators) into fast multiphysics surrogate models.

1. Lead inverse-design using active learning and Bayesian optimization to cut physical trial-and-error and the number of DOE runs.

1. Design and maintain versioned, high-concurrency simulation data pipelines and an MLOps workflow.

Requirements:

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.

1. 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.

1. 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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