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PhD Student (f/m/d) for Thesis Topic: HD Maps for ADAS AI

Location Wuppertal, North Rhine-Westphalia, Germany

Job ID J000632812

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In more and more cars, a variety of sensors are installed for environmental perception to enable Advanced Driver Assistance Systems and Automated Driving Features. In addition to passive sensors (e.g. cameras), active sensors such as LiDAR and radar are increasingly used. Thereby every sensor comes with its strength and weaknesses. Every individual recording can only capture limited information about the environment, while many applications benefit from having a "condensed" representation over multiple recordings and sensor data about the environment. 

Therefore we aim to aggregate data from multiple sources, into a unified world representation (HD Map). We investigate how a "condensed" model of the environment can be derived from the variety of sensor data that could be taken by different vehicles at different points in time. During your PhD you will develop new data processing and Machine Learning methods to derive insights from a large and ever growing body of real world recording data. Particular focus will be placed on the distinction between static objects (e.g., traffic lights) and dynamic objects (e.g., cars), as these must be treated differently.

Where the aggregation of static objects lay the foundation, the smart aggregation of dynamic objects and environments can inform a multitude of scenarios relevant to scene understanding. Behavior derived from dynamic objects as well as change detection are topics of interest in your research. The question whether a lane has become unavailable due to construction work, can thereby serve as an example for an automatically deducted scenario from available data.

We Offer

  • Funding for a 3-year full-time Ph.D. scholarship in cooperation with a university

  • The opportunity to work on state-of-the-art machine learning solutions in a research-oriented environment

  • Hands-on work with real-world data to develop and test your machine learning solution

  • A strong, supportive global team of highly educated machine learning engineers to contribute and support you

Your Profile

  • You hold a master’s degree in Computer Science, Engineering or similar

  • You have practical experience with at least one ML framework like Tensorflow, Keras, Caffe, PyTorch, etc

  • You can program self-reliant in Python and/or C++

  • You have good English skills (German skills would be a plus)

  • You are looking for a challenging task, bring a high level of self-motivation and like to be part of a team


  • Great! Please apply and include your CV and (important!) a grade overview.

Some see differences. We see perspectives that make us stronger.

Diversity and Inclusion are sources of innovation and creativity, both of which are essential to Aptiv’s success. Everyday our diverse team comes together, drives innovation, pursues solutions, and meets challenges using their unique abilities, perspectives and talents, changing what tomorrow brings. When you join our team, you’ll get encouraged to think boldly, express your viewpoint and innovate as a matter of habit.

Some See Technology. We See a Way to Make Connections.

‘We are one of the largest vehicle technology suppliers and our customers include the 25 largest automotive original equipment manufacturers (“OEMs”) in the world. We operate 127 major manufacturing facilities and 12 major technical centers utilizing a regional service model that enables us to efficiently and effectively serve our global customers from best cost countries. We have a presence in 46 countries and have approximately 18,900 scientists, engineers and technicians focused on developing market relevant product solutions for our customers’

Applications from severely disabled persons and persons of equal status will be given preferential consideration in the event of equal suitability.

Privacy Notice - Active Candidates:

Aptiv is an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, gender identity, sexual orientation, disability status, protected veteran status or any other characteristic protected by law.

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