Researcher (m/f/d) – AI-Enhanced Streaming Data Systems | Researcher (m/f/d) – AI-Enhanced Streaming Data Systems

Ruhr-Universität Bochum

Bochum, Nordrhein-Westfalen, Deutschland
Published Feb 10, 2026
Full-time
Fixed-term

Job Summary

As a Researcher at the Database Systems Chair of Ruhr-Universität Bochum, you will join a new research group dedicated to pioneering AI-enhanced streaming data systems. Your day-to-day work involves developing learning-based methods to augment or replace classical components in modern stream processing engines, with a focus on cost modeling, resource allocation, and runtime adaptation. You will explore the integration of Machine Learning and Large Language Models (LLMs) into streaming pipelines and implement experimental prototypes. This position is highly attractive for those aiming for an academic or high-level R&D career, as it offers the opportunity to publish in top-tier international venues like SIGMOD, VLDB, and NeurIPS. You will work in a collaborative environment focusing on practical systems research, contributing to the next generation of multimodal data processing while potentially participating in university teaching activities.

Required Skills

Education

Excellent university degree (Master’s or equivalent) in Computer Science or a closely related field.

Experience

  • Professional experience or basic knowledge in database systems, stream processing, or distributed systems
  • Experience in programming with Python or similar languages
  • Experience with Linux-based development environments
  • Experience in designing and implementing experimental prototypes and system extensions
  • Experience in conducting experimental evaluations on streaming and multimodal workloads
  • Experience in scientific writing and publishing research results is preferred

Languages

English (Fluent)

Additional

  • This is a fixed-term position for 36 months starting May 1st, 2026. Applicants must apply by February 28, 2026. The role is based at Ruhr-Universität Bochum (RUB) with a full-time workload of 39.83 hours per week.