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PhD Student (f/m/d) for Thesis Topic: NLP for Visual-to-Language models

Location ヴッパータール, Nordrhein-Westfalen, ドイツ連邦共和国

求人ID J000645547


APTIV is one of the largest automotive suppliers and a global technology leader, with more than 190,000 people worldwide. We address mobility’s toughest challenges and deliver market-relevant solutions for our customers. One key technology for further pushing the boundaries of advanced driver assistance systems and autonomous driving is machine learning/artificial intelligence (AI). We are searching for talented students who bring in new ideas and help to develop our next generation of AI algorithms.

We are developing algorithms which, based on camera, lidar and radar sensor data, can understand complex driving scenarios (e.g. crowded multi-lane highways, inner cities), anticipate the behavior of other road users (e.g. pedestrian crossing the road, vehicle changing the lane or taking a turn) and finally make safe and comfortable driving decisions.

Are you (almost) finished with your Master's degree and want to pursue a PhD? Do you want to bridge the gap between academics and industry and apply your scientific expertise to real challenges in the area of automated and autonomous driving?

Your Role

With the growing amount of data in different areas it is becoming more and more challenging to identify the desired data in a potentially large pool of data. Thus, techniques are required to capture the semantics of data (e.g., images or point clouds) in an automated way.

In this thesis a method should be developed to represent the semantics of images of street scenes (e.g., a pedestrian crossing in front of car, overtaking maneuvers, etc.) in a way that it is possible to query that information. Natural Language Processing (NLP) techniques shall be used for this and the algorithm shall also be integrated in an existing data warehousing solution. Thus, the focus shall not only be on retrieving the best possible selection of images but also on efficient implementation.

Your Profile

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

  • You have practical experience with NLP and 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

We Offer

  • Funding for a 3-year full-time Ph.D. scholarship in cooperation with a university
  • Opportunity to work on state-of-the-art machine learning solutions in a research-oriented environment

  • Hands-on work with real-world data

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


  • 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.

At Aptiv, we don’t just see the world differently; we work to change reality. That means developing technology that rewrites the rules of what’s possible in the pursuit of making transportation safer, greener and more connected. Today there are more than 190,000 of us globally, located in 44 countries, and united by one mission. Join the movement and together, let’s change tomorrow.

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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