Master thesis AI-Driven Geospatial Data Fusion Leveraging Machine Learning (f/m/x)

Descripción del puesto:

SOME IT WORKS. SOME CHANGES WHAT'S POSSIBLE.

SHARE YOUR PASSION.

More than 90% of automotive innovations are based on electronics and software. That's why creative freedom and lateral thinking are so important in the pursuit of truly novel solutions. And that's why our experts will treat you as part of the team from day one, encourage you to bring your own ideas to the table - and give you the opportunity to really show what you can do.

With the proliferation of geospatial technologies and digital mapping, data sources such as road networks, traffic conditions, lane configurations, and GPS-based sensor data are being generated at an ever-increasing rate. The integration of this data for road network modeling faces several challenges like heterogeneity, fragmentation, and noisiness of data as well as the difficulty to capture the complexity of the road network topological relationships.

What awaits you?

* You contribute to advancing autonomous driving technologies through the integration of geospatial data.

* In your master's thesis, you explore innovative solutions to fuse diverse geospatial data sources, addressing challenges such as data heterogeneity and complexity in road network modeling.

* Here, you leverage a hybrid approach combining Graph Neural Networks and Graph Attention Networks to automatically discover patterns in road networks and model map data at a semantic level.

* Additionally, you develop a knowledge graph to capture the semantics of geospatial data, facilitating the integration of graph embeddings with machine learning models for enhanced performance and efficiency.

Please note that your thesis must be supervised by a university on your part.

What should you bring along?

* Studies in machine learning, data analysis, or a related field.

* Proficiency in programming languages such as Python, along with experience in using libraries and frameworks for machine learning and graph-based models.

* Familiarity with graph theory and neural network architectures, particularly Graph Neural Networks and Graph Attention Networks.

* A proactive mindset and problem-solving skills to tackle complex challenges in data integration and modeling.

* A passion for innovation and a desire to contribute to the future of mobility through advanced data-driven solutions.

Do you have an enthusiasm for new technologies and an innovative environment? Apply now!

What do we offer?

* Comprehensive mentoring & onboarding.

* Personal & professional development.

* Flexible working hours.

* Mobile work.

* Attractive & fair compensation.

* Apartments for students (subject to availability & only at the Munich location).

* And much more, see bmw.jobs/whatweoffer.

Start date: from 08/11/2025
Duration: 6 months
Working hours: Full-time

Do you have questions? Then submit your inquiry easily via our contact form. Your request will be answered by phone or email afterwards.

At the BMW Group, we place great importance on equal treatment and equal opportunities. Our recruiting decisions are based on the personality, experience, and skills of the applicants. Learn more here