Working Student: Machine Learning for Semiconductor Layouts

**Take responsibility**: You support the team to turn semiconductor layout data (GDSII) into actionable insights by building clear, reliable Python scripts that read, organize, and prepare structures for analysis
- **Focus on the future**:You develop and evaluate straight forward machine learning approaches to categorize layout structures and highlight patterns of interest for our design and reliability teams
- **Data is everything**:You establish a simple, easy-to-use data backbone that stores results and enables fast search and retrieval of specific structures and attributes
- **Being strong together**:You create lightweight tools and visuals (e.g., command-line or simple UI) that make it easy for colleagues to explore, filter, and understand layout findings
- **Teamwork is dreamwork**:You collaborate with domain experts to define structure categories, quality criteria, and a pragmatic roadmap from prototype to every day usage
- **Stakeholder management**:You document your work clearly and present progress and recommendations to stakeholders on a regular cadence
- **Shape the future**:You will participate in exploring the next step in automated GDS creation (template-based or parameterized building blocks) to support faster design workflows.

**Your Profile**
- **Study field**: You are currently studying Computer Science, Electrical Engineering, Physics, Mathematic or similar
- **Interests**:You have an interest in semiconductor design data and are eager to learn how layout structures and design rules influence product quality and reliability
- **Way of working**:You communicate clearly, document thoughtfully, and can translate technical details into practical next steps
- **Personality**:You are proactive, curious, enjoy working in a team, and are good at dealing with a wide range of opinions and turning ambiguities into tangible results
- **Language skills**: You have good and English skills, both written and spoken; German is a plus
- CV in English
- Certificate of enrollment at university
- Latest grades transcript (not older than 6 months)
- High school report

Important information:

- **Working part-time**: The focus is on studies. Therefore, working student is possible during the lecture period with a maximum of 20 hours per week.
- **Proper students (according to the German law) are welcome**: You must be enrolled, and the examination results or modules of your studies must not have been completed yet, so that you can still work in our team for at least 6 months. You must also not be in a semester of leave.
- **You should live close to the site**: It is important for us to work with you on site and to integrate you into the team. You should therefore be able to come to the site regularly.

**Further links**:
Find out what we are looking for in your CV
Discover our student website

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