CRS System Engineer Test Bench / 高压共轨系统测试工程师 岗位关键词/Key Words:  高压共轨，液压，功能，诊断互联，标定，测试技术，仿真，设备，实验室

工作地点：无锡

岗位职责/Job Responsibilities:

 先进测试技术的开发与系统测试台架及设备应用 – 基于产品测试需求开发先进的系统测试技术，引入先进的 用于系统测试台架的设备，编制自动测试程序，撰写测试流程指导，开发测试数据处理工具等

Be able to conduct the development of advanced test techniques, introduce advanced test devices for system test benches, program automatic test sequences, document test work instructions, develop test data process tools.

 

 液压系统标准测试标定、高低压回路性能研究及系统功能能的开发和验证 – 基于共轨系统试验台架上搭建指 定的高压共轨系统，能够独立完成用于项目标定的标准测试，喷油器的喷油特性、系统高低压回路的流量/压 力/温度特性、控制器的软件功能、系统的驱动功耗特性的测试研究及评价等

Be able to conduct common rail system standard tests for project calibration, fundamental investigations and evaluation on injection characteristic, system high & low pressure circuit flow rate/pressure/temperature, ECU software functionalities, system energy consumption, etc.

 

 系统仿真 – 利用 AMESim 软件完成共轨部件的系统集成以及相关功能的性能仿真

Be able to conduct the development simulation method with AMESim for system functionalities via system models integration  

 

 系统软件功能如液力系统喷射精度，产品互联预诊断功能开发 – 基于产品应用的市场特点及产品互联预诊断 的需求，提出解决方案及功能概念创新，并运用大数据分析开展可行性研究，开发对应的控制单元的软件功能

Be able to conduct innovative ideas and concepts for solutions of product application market features and connectivity requirement like FIE injection quantity, Pre-diagnosis, study the feasibility based on big data of product and develop the ECU software functions

  岗位要求/Job Requirements:  

1. 教育背景：硕士或以上学历，机电工程或控制工程类等专业背景

Master degree or above in Mechanic & Electrical Engineering or Control Engineering from a good university.

2. 英文：CET-6级，良好的英文书写及口语技术交流能力 CET6, Good English in both spoken and written for technical communications

3. IT 技能：MS Office, VBA, MATLAB 等至少精通一门编程语言 MS office, VBA, MATLAB, etc, and at least skilled in one programming language

4. 能力要求：  良好的沟通表达能力，团队合作，善于解决问题 Good communication and team work ability, good problem solving skills.

工作积极主动，善于合理分配时间 Pro-active, and excellent time management skills.

学习新知识很快, 有广泛的技术背景 Quick learner with broad technical background 

有神经网络(如模式识别、深度学习)经验优先 Experiencing in neural network (e.g. pattern recognition, deep learning) is preferred