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# Masterarbeit Plausibilization of ADAS Front Camera Impairments Using Machine Learning

- **Company:** Dr. Ing. h.c. F. Porsche AG
- **Location:** Weissach, Germany
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Python (Programming Language), Machine Learning, Microsoft Office, Software Engineering, Git, Pandas, Information Technology
- **Published:** July 22, 2026
- **Apply:** https://de.indeed.com/viewjob?jk=cc587ebd237e100d

## About the Role

* Computer Science
 * Artificial Intelligence
 * Robotics
 * Vehicle Technology
 * Math, Data Science or comparable degree program

Studienschwerpunkte:

 * Software development / programming
 * Machine learning / statistics / data science

Fachkenntnisse:

 * Solid understanding of common machine learning concepts
 * Early experience with statistical models
 * Experience with working on tabular data

IT-Kenntnisse:

 * Confident use of Microsoft Office and Git
 * In-depth knowledge of Python (esp. Pandas)
 * Experience with popular statistical frameworks

Sprachkenntnisse:

 * English (fluent spoken and written)
 * German is an advantage

Sonstiges / Soft Skills:

 * High level of initiative
 * Strong analytical skills
 * Structured and independent way of working
 * Ability to work in a team
 * Goal-oriented

## Description

This thesis investigates whether and to what extent ADAS front camera impairments can be plausibilized without relying on image data, using machine learning on contextual, environmental, and vehicle-based signals.
Starting from a large collection of front camera recordings with suspected impairment events, the work shall analyze recurring patterns and cluster different impairment types together with their likely causes, such as fogging, icing, or low sun conditions. A central objective is to determine which impairment hypotheses can be inferred from context information alone - and under which conditions this is possible in a reliable and explainable way.

Based on these findings, an interpretable model or algorithm shall be developed that assesses the plausibility of different impairment causes and provides transparent reasoning for its decisions. The approach shall be evaluated on existing fleet data and compared against image-based indicators or existing heuristics where available, with the goal of enabling scalable and trustworthy impairment analysis on large real-world datasets.

 * Kennziffer: J000021231
 * Einstiegsart: Abschlussarbeit
 * Einsatzort: Weissach
 * Gesellschaft: Dr. Ing. h.c. F. Porsche AG

Aufgaben
AufgabenAufgaben

 * Determine which impairment hypotheses can be inferred from context information alone
 * Develop an interpretable model that assesses the plausability of different impairment causes
 * Evaluate the model on internal vehicle data


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