Meta AR/VR Job | Battery Engineering Intern, Cell Modeling (BS/Masters)
Job（岗位）: Battery Engineering Intern, Cell Modeling (BS/Masters)
Type（岗位类型）: Hardware
Citys（岗位城市）: Sunnyvale, CA
Date（发布日期）: 2024-12-19
Summary（岗位介绍）
At Meta Reality Labs, our goal is to explore, innovate and design the hardware for the next generation of virtual, augmented, and mixed reality experiences. The mission of the battery team is to develop power sources for the devices that enable these experiences.
We are looking for an experienced and motivated Battery Modeling Intern to join our team who specializes in leveraging the principles of electrochemistry to maximize the utilization of Li-ion batteries in our products. In this role, you will develop battery cell models and identify ways to optimize the modeling process to generate accurate models quickly in order to aid the design iteration process. You will apply your knowledge, experience and expertise to solve complex engineering and technological challenges.
Our internships are twelve (12) to sixteen (16) weeks long.
Qualifications（岗位要求）
Bachelors Degree in engineering, materials science, chemistry, physics, or related field
Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment
Experience with equivalent circuit modeling of Li-ion cells, including parameter estimation, using commercial modeling tools such as MATLAB, Python, etc.
Knowledge of Li-ion technology, electrochemistry, and battery testing
Interpersonal skills: cross-group and cross-culture collaboration
Description（岗位职责）
Collaborate with cell, algorithm, and validation engineers to generate multi-RC equivalent circuit models of battery cells
Propose ways to experimentally identify parameters quickly and efficiently and validate accuracy of models
Leverage insights from electrochemistry to propose newer modeling techniques and compare efficacy of various techniques
Additional Requirements（额外要求）
Currently has, or is in the process of obtaining a Masters or PhD degree in engineering, materials science, chemistry, physics, or related field
Experience with using data driven AL/ML algorithms for battery control and battery health diagnostics
Knowledge of control and estimation theory, model order reduction techniques and their application to Li-ion battery modeling.
Knowledge of efficient optimization methods and adaptive control techniques for real-time battery management suited for implementation on memory constrained ICs
Familiarity with cell/battery testing equipment (Maccor, Arbin, Neware, BioLogic, or equivalent)
Intent to return to degree-program after the completion of the internship/co-op
