# Intern (Master's Thesis) Manufacturing

**Company**: Ford Motor Company
**Location**: Cologne, North Rhine-Westphalia
**Work arrangement**: onsite
**Job type**: full-time
**Category**: Manufacturing
**Industry**: Automotive
**Wikidata**: https://www.wikidata.org/wiki/Q44294

**Apply**: https://efds.fa.em5.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1/job/72826?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_fcfdb76d-ee3

## Description

As a German subsidiary of the Ford Motor Company, Ford-Werke GmbH shapes the future of mobility. In the Global Operations area, teams worldwide work on making production processes efficient and future-oriented.

The ATP (Advanced Industrial Technology and Platforms) organisation deals with innovations in manufacturing.

## Responsibilities

Your tasks will include:

- Preparing, checking, and analysing real production data, including data cleaning, feature extraction, and visualisation

- Comparing and correlating real production data with existing process and simulation models, as well as relevant quality characteristics

- Building and adapting a data-based model that reflects real production conditions and process fluctuations

- Analysing the model's generalisability across different production conditions, plants, or materials, and identifying limiting factors

- Investigating and evaluating suitable transfer learning approaches to efficiently adapt existing models to new production domains and reduce required training effort

- Validating the model's quality using real production data and assessing its potential for scalable industrial application

## Qualifications

Requirements include:

- Studying mechanical engineering, industrial engineering, physics, computer science, computational engineering science, or a similar field

- Good knowledge of Python and previous work experience in data science and machine learning

- Familiarity with data processing and visualisation; experience with industrial or production data is beneficial

- Analytical, structured, and independent work style

- Teamwork and enthusiasm for collaboration with Ford and Fraunhofer ILT

- Excellent German and/or English language skills in writing and speaking

## Benefits

Benefits include:

- A challenging thesis at the interface of AI, manufacturing, and real industrial production

- Collaboration with Global Operations within the ATP organisation and Fraunhofer-Institut für Lasertechnik ILT

- Access to real production data and insights into innovative manufacturing technologies

## Skills

### Required
- Python
- Data Science
- Machine Learning
- Data Processing
- Data Visualisation

### Nice to have
- Industrial Data
- Production Data
- Transfer Learning

---

Source: [Apply at efds.fa.em5.oraclecloud.com](https://efds.fa.em5.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1/job/72826?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)