PhD Student AI Production (f/m/d) - #936812

Work environment

Are you creative, innovative, and experimental? Are you interested in metallic materials science and modern manufacturing technology? Do you want to contribute to resource-efficient and ecological mobility for the future? Then you are exactly right in our multidisciplinary team at Volkswagen Group Innovation. We research for a sustainable future for all brands of the Volkswagen Group, examining the entire process chain from material development to recycling. During your three-year doctoral studies, you will develop an AI method to distinguish good components from defective ones based on the data available in production. You will work closely with steel manufacturers and colleagues from production. Advanced knowledge in Machine Learning (ML) and Artifical Intelligence (AI) is desirable.

Possible Tasks within this Role

• Familiarizing with the topic by determining the state of science and technology
• Independently selecting data and methods to carry out the task
• Using Machine Learning methods to optimize production
• Evaluating test series in collaboration with colleagues from the Volkswagen Group
• Preparing results and presenting them in internal and external environments
  
  

The final doctoral topic is defined together with the professor. The prerequisite for the cooperation is the confirmation of supervision as well as a confirmed delimitation of topics by the professor of a university or research institution entitled to confer a doctorate.

Qualification Requirements

• Good to very good university degree with eligibility for doctoral studies in the fields of Materials Science, Mechanical Engineering, or Computer-Aided Technologies
• Knowledge in the field of steel materials and sheet metal forming
• German or English language level B2
• Quick comprehension, high level of commitment, precise and conscientious working style
• Willingness to communicate with colleagues from all disciplines
  
  

Keywords

Quality Assurance, Crack Detection, Big Data, Waste Reduction, Lean Factory, Research