Liebherr-Digital Development Center GmbH
Thesis time series databases - Cloud Backend(m/w/x)
Full-timeWorking StudentWith Homeoffice
Ulm
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BMBMW Group
Thesis on AI-Driven Map Matching and Path Prediction on Semantically Enriched Road Networks(m/w/x)
Ulm, München
Full-timeInternshipWith Home Office
AI/ML
Data Science
Nejo AI Summary
AI-driven map matching and path prediction for autonomous driving using RDF and graph embeddings. Machine learning background with Python and PyTorch/TensorFlow required. Flexible hours, mobile work, student apartments.
Requirements
- Studies in computer science, data science, or electrical engineering
- Solid background in machine learning (supervised, basic reinforcement)
- Experience with Python and ML/DL frameworks (PyTorch, TensorFlow)
- Experience with data processing libraries
- Familiarity with graph representations and knowledge graphs (RDF, SPARQL)
- Comfort with graph or knowledge graph embeddings is a plus
- Understanding of graph neural networks or representation learning
- Software engineering skills for reproducible experiments
- Version control, clean code, and benchmarking skills
- Good German language skills
- Good English language skills
Tasks
- Support modeling road connectivity and constraints in RDF
- Implement rule sets for computing probable paths
- Build features from observations and road graph data
- Learn graph embeddings and train models for path prediction
- Formulate path prediction as a reinforcement learning problem
- Integrate graph embeddings and train DQN or actor-critic agents
- Develop sequence-to-sequence or transformer models for GPS trajectory alignment
- Compare models to baseline methods
- Experiment with graph-aware attention and constrained decoding
- Design metrics and scenarios for accuracy, robustness, efficiency, and interpretability
- Run ablation studies on embeddings and semantic attributes
Education
- Currently in higher education
Languages
- German – Advanced
- English – Advanced
Tools & Technologies
- Python
- PyTorch
- TensorFlow
- RDF
- SPARQL
Benefits
Mentorship & Coaching
- Comprehensive mentoring and onboarding
Learning & Development
- Personal and professional development
Flexible Working
- Flexible working hours
- Mobile work
Other Benefits
- Student apartments
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of BMW Group and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
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BMBMW Group
Thesis on AI-Driven Map Matching and Path Prediction on Semantically Enriched Road Networks(m/w/x)
Ulm, München
Full-timeInternshipWith Home Office
AI/ML
Data Science
Nejo AI Summary
AI-driven map matching and path prediction for autonomous driving using RDF and graph embeddings. Machine learning background with Python and PyTorch/TensorFlow required. Flexible hours, mobile work, student apartments.
Requirements
- Studies in computer science, data science, or electrical engineering
- Solid background in machine learning (supervised, basic reinforcement)
- Experience with Python and ML/DL frameworks (PyTorch, TensorFlow)
- Experience with data processing libraries
- Familiarity with graph representations and knowledge graphs (RDF, SPARQL)
- Comfort with graph or knowledge graph embeddings is a plus
- Understanding of graph neural networks or representation learning
- Software engineering skills for reproducible experiments
- Version control, clean code, and benchmarking skills
- Good German language skills
- Good English language skills
Tasks
- Support modeling road connectivity and constraints in RDF
- Implement rule sets for computing probable paths
- Build features from observations and road graph data
- Learn graph embeddings and train models for path prediction
- Formulate path prediction as a reinforcement learning problem
- Integrate graph embeddings and train DQN or actor-critic agents
- Develop sequence-to-sequence or transformer models for GPS trajectory alignment
- Compare models to baseline methods
- Experiment with graph-aware attention and constrained decoding
- Design metrics and scenarios for accuracy, robustness, efficiency, and interpretability
- Run ablation studies on embeddings and semantic attributes
Education
- Currently in higher education
Languages
- German – Advanced
- English – Advanced
Tools & Technologies
- Python
- PyTorch
- TensorFlow
- RDF
- SPARQL
Benefits
Mentorship & Coaching
- Comprehensive mentoring and onboarding
Learning & Development
- Personal and professional development
Flexible Working
- Flexible working hours
- Mobile work
Other Benefits
- Student apartments
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of BMW Group and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
Like this job?
BetaYour Career Agent finds similar jobs for you every day.
About the Company
BMW Group
Industry
Automotive
Description
Das Unternehmen bietet spannende Praktika im Bereich Markenerlebnis und Eventmanagement und legt großen Wert auf Gleichbehandlung und Chancengleichheit.
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