Thesis on AI-Driven Map Matching and Path Prediction on Semantically Enriched Road Networks

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

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About this role

Thesis on AI-Driven Map Matching and Path Prediction on Semantically Enriched Road Networks Location not specified 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. 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. Map matching and path prediction are core capabilities for autonomous driving. Our team at the BMW Group explores data-driven approaches that combine symbolic reasoning and machine learning, operating on real-world map data to improve robustness, accuracy, and interpretability in complex urban environments. What awaits you? You will support modeling road connectivity and constraints in RDF and implementing rule sets to compute the most probable path using a rule-based reasoner. Furthermore, you help build features from observations and the road graph, learning graph embeddings and training models to predict the next link or path. In addition, you contribute to formulating path prediction as a reinforcement learning problem, integrating graph embeddings and training agents such as DQN or actor-critic. Moreover, you will assist in developing sequence-to-sequence or transformer-based models to align GPS trajectories to graph-aligned edge sequences and comparing them to baselines. Darüber hinaus wirkst du mit beim Experimentieren mit graph-aware atten

Read the full description at www.bmwgroup.jobs. FewerJobs shows a preview and links to the original posting.