Python programozó diákmunka állások Németországban (adatbázisszakértő)

As part of our team, you'll help push the state-of-the-art in model scalability by contributing to the core systems for our products....

• Develop methods and workflows to construct robust co-regulation networks from large single-cell and spatial transcriptomics datasets
• Integrate ontologies and metadata (e.g., tissue, cell type, developmental stage, treatment) to build tissue- and context-specific co-regulation networks

• Prepare large-scale automated driving datasets for training and evaluation purposes.
• Contribute to the development and maintenance of the coding framework for data processing, model training, and validation of end-to-end driving systems.

• Build Fullstack Features: Implement user-facing features using modern frameworks and connect them to backend APIs.
• Backend Development: Contribute to Python-based backend services (Django REST or FastAPI), including API endpoints and business logic.

• You will contribute to improving our foundation recommender system
• Your role involves exploring and evaluating new machine learning methods, including applications of LLMs for recommender systems
• Expect to support personalized CRM communications (push notifications, in-app messages, newsletters)

• Develop computer vision models to analyze surgical videos, including action recognition, instrument usage, and relevant interactions
• Build and annotate a suitable dataset in close collaboration with other project staff; develop annotation and quality-assurance tools

You will design and deliver end-to-end ADAS AI models such as object detection, behavior prediction, and trajectory planning - from data analysis and model design to SOP-ready release....

• Research on learned components for streaming data systems, such as
• learned cost, latency, and throughput models for continuous and multimodal queries,
• learning-based scheduling, parallelism, and resource allocation for streaming operators, and

• Architect and build the high-performance backbone of our data platform
• Focus on distributed computing stability and building effective caching strategies for large-scale operations
• Design ETL pipelines and architect resilient caching for complex dependency resolutions

to generate and harmonize user-friendly, standardized data analysis pipelines for a variety of computational tasks, including, but not limited to, mutation calling, bulk and single-cell RNA-seq analysis, ChIP-seq / Cut&Run analysis, CRISPR screen analysis, and DNA barcoding analysis,...

You build and maintain end-to-end ML pipelines using workflow orchestration tools: from data ingestion to distributed training, evaluation, model compilation, and deployment-ready artefacts....

You design and build high-performance search and query pipelines over PB-scale MDF4 and MCAP data lakes, enabling ML engineers to find relevant driving scenarios, sensor conditions, and edge cases across billions of records in seconds....

• Design and implement scalable data engineering and analytics solutions in Azure and Databricks.
• Translate business and user requirements into technical architecture, concepts, implementation and roadmaps.

Strengthen the completeness and reliability of the Group's Data Catalog (Data Galaxy) by conducting in depth analyses of data systems, governance frameworks, and business usage across the organization...

• Collaborate with cross-functional teams to define data requirements and ensure data quality
• Participate in monitoring, troubleshooting, and resolving data processing issues

NEW SOLUTIONS EMERGE WHEN THINKING ISN'T HARD-WIRED. 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 new solutions. Naturally, this includes flexible working models, so that our...

• Architect and build the high-performance backbone of our data platform
• Focus on distributed computing stability and building effective caching strategies for large-scale operations
• Design ETL pipelines and architect resilient caching for complex dependency resolutions

• Architect complex data flow solutions, enabling transparent, versioned, and fully auditable computations
• Design the data layer by developing metadata and data lineage models fused by semantic graphs and numerical data