At BMW Group, you will explore a data-driven approach that combines symbolic reasoning and machine learning techniques to effectively match noisy geolocation data and compute the car’s Most Probable Path (MPP), utilizing real-world data represented as an RDF knowledge graph.
What awaits you?
- You employ a rule-based reasoner utilizing map data represented as an RDF knowledge graph to accurately calculate the Most Probable Path (MPP).
- Here, you implement a supervised learning machine learning model to directly predict the MPP based on observed data and road networks, often eliminating the need for hand-crafted rules or extensive domain knowledge.
- Additionally, you utilize a reinforcement learning ML model to train an agent for calculating the MPP.
- You apply suitable machine learning models, such as sequence-to-sequence or transformer-based architectures, to map GPS trajectories onto a digital map represented as a knowledge graph.
- Furthermore, you conduct a comparative analysis of the results, drawing conclusions about the advantages and disadvantages of each approach to inform future developments.
Please note that your thesis must be supervised by a university on your part.
What should you bring along?
- Studies in computer science, data science, or a related field.
- A solid foundation in machine learning and data analysis.
- Proficiency in programming languages such as Python, along with experience in machine learning libraries and frameworks.
- Familiarity with symbolic reasoning, graph theory, and neural network architectures, particularly in the context of Graph Neural Networks and knowledge graphs.
- Experience with supervised and reinforcement learning techniques, as well as an understanding of graph embedding methods.
- Strong analytical and problem-solving skills, enabling you to tackle complex challenges.
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.
- Digital offers & mobile working.
- Attractive & fair remuneration.
- Apartment offers for students (subject to availability & only Munich).
- And many other benefits - see jobs/benefits
Earliest starting date: 08/11/2025
Duration: 6 months
Working hours: Full-time
Do you have any questions? Then simply send your enquiry using our contact form. Your enquiry will then be answered by telephone or e-mail.
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.
