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【实习】苹果Brittle MQE-VisionSystem Development

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发表于 2021-10-14 08:32:51 | 显示全部楼层 |阅读模式

苹果实习,做machine learning相关,地点Shanghai/Shenzhen/Suzhou,详见附件

有兴趣的同学可以联系邮箱qiaoyan_wang@apple.com


If you are the kind of students who are passionate on pursuing excellence, embracing challenges, enjoying work with others, learning new things along the way, Apple is the right place for you.

Apple MQE is responsible for developing, applying and maintaining quality standards for materials, processes and products, solving quality engineering problems in creative and effective way


Education/Experience Requirements

• Master or above, Mechanical Engineering & Automation/Computer Science/Industry Design/ Applied Mathematics/Optics and Fine Mechanics or equivalent major is preferred

• Excellent written and oral English communication skills

• Clear and logical thinking

• Intern Duration 3~6 months full time

• Working Location: Shanghai/Shenzhen/Suzhou

• 90% business travel @ supplier factory


Technical Qualifications

• Fundamental knowledge on computer vision / machine learning techniques to build models that are deployable into a manufacturing environment with some time in project management

• Technical skills in Machine or Computer Vision as well Machine Learning (CNNs, object detection)

• Knowledge of basic networking concepts and protocols, e.g.: PLC,TCP/IP, HTTP, etc

• Knowledge of C++, Python, CLI, Linux and Unix shell scripting would be preferred

• Experience of machine learning and statistical-analysis libraries would be preferred

• Experience with 2D/3D triangulation laser system and/or CCD inspection system

• Familiar with mechanical metrology system evaluation process, such as GRR, Correlation, Stability and Reliability

• Understanding of optical, software filtering and judgment algorithms

• Collaborative & project management skill will be key in excelling in this role


Roles & Responsibility

• Working on machine vision measurement system and DFM review

• Increase the cosmetic defects detection rate of cosmetic AOI

• Use machine learning/deep learning model/Data mining to train the AOI and reduce the misjudgment rate

• Work with projects MQE to qualify and roll in inspection machines into mass production

• Explain and present analysis and technical model to broad technical audience


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