诚聘 Tesla- react native

vivianvivian1111 2022-10-09 16:41:04

划重点:要有过react native的经验(2年以上)+英语口语能够沟通
可以把简历发至:xuwenb@tesla.com 或者直接加微信:vivianxwb

Base上海&北京

THE ROLE

The Sales & Delivery application team is responsible for building applications to support Tesla’s rapid growth while providing an amazing customer experience. The business covers the whole customer journey including discovery, ordering, vehicle fulfillment, ownership, charging and service. The application scope covers Tesla Web, App, Mini Program, internal tools, Tesla for Business and new product launch. The mission of our team is to streamline business operations, decrease costs, improve customer experiences, and solve the business challenges from both product and technology perspective. Our engineers are hands-on and encouraged to own their own projects, contribute to new ideas, and make an impact on the way that our company operates.

RESPONSIBILITIES

• Design, code and maintain mobile user experiences end-to-end.

• Optimize code for performance, stability and maintainability.

• Work with a cross-functional team of hardware engineers, application/UI software engineers, QA/Validation, and designers.

REQUIREMENTS

• Bachelor’s Degree in Computer Science, Software Engineering, or similar areas of study.

• Minimum 5 years’ experience in Mobile (iOS/Android) development.

• 2+ years building web and/or mobile experiences with evidence of exceptional ability.

• Proficient with React Native and Redux.

• Proficient with TypeScript is a plus.

• Familiar in Objective-C or Swift or Kotlin native app development.

• Excellent grasp of fundamental computer science concepts, good at solving complex technical problem.

• Experience using common design patterns. High standards for code quality, maintainability, and performance.

• Experience creating, maintaining and shipping top-ranking mobile apps is preferred.

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内容概要:本文系统介绍了基于Matlab构建的简化单粒子(SPM)电化学模型及其参数化方法,聚焦于锂离子电池的降阶电化学模型P2D的简化实现,涵盖模型建立、参数辨识、测试数据提供及仿真验证全流程。资源核心在于深入剖析电化学模型的关键参数提取与优化过程,帮助科研人员理解电池内部反应机理与数学建模范式,支持后续的模型扩展与工程应用。文档不仅提供了完整的SPM模型代码与参数拟合工具,还整合了丰富的科研辅助资源,包括智能优化算法、机器学习、电力系统管理、路径规划、信号处理等多个领域的Matlab/Simulink仿真案例与Python实现方案,极大拓展了该模型在电池健康状态(SOH)估计、寿命预测、充放电控制策略等方向的应用潜力。; 适合人群:具备一定Matlab编程能力,从事新能源技术、电化学建模、电池管理系统(BMS)、储能控制、自动化仿真等相关领域的研究生、科研人员及工程技术人员。; 使用场景及目标:①开展锂离子电池电化学模型的建模与参数辨识研究;②实现P2D与SPM降阶模型的仿真与实验验证;③结合实测数据进行模型参数拟合与精度优化;④拓展应用于电池老化分析、SOH估算、充放电策略设计及储能系统动态响应研究。; 阅读建议:建议读者按照文档结构循序渐进学习,重点研读SPM模型构建与参数辨识章节,结合所提供的测试数据与代码进行动手实践,并积极借鉴附带的智能算法与机器学习模块以提升模型鲁棒性与预测精度。

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