Research Associate (E13, DIMA, IV-386/26)
BIFOLD/TU Berlin's DIMA Group hiring researche associate in data management and scalable systems
About us
The Berlin Institute for the Foundations of Learning and Data (BIFOLD) at TU Berlin is seeking a research assistant for an agility subproject with expertise in distributed stream processing and edge-cloud systems at the intersection of sensing, machine learning, and systems integration. Potential applicants should, above all, have practical experience with data stream processing systems. The project will run jointly with Prof. Volker Markl, Dr. Steffen Zeuch, Dr. Thanasis Georgiadis, and the rest of the NebulaStream team in the Database Systems and Information Management (DIMA) research group.
In the project “Efficient, Automated and Real-Time Sensor-Edge-Cloud Stream Processing for Smart-Manufacturing (SEC-SM),” a smart manufacturing streaming architecture based on the ACA-Loop (Analyze-Condition-Act) for NebulaStream is to be developed. An architecture that can continuously analyze multimodal sensor and camera data of varying modalities, reliably detect critical findings, such as errors and discrepancies in manufacturing tasks with low latency, automate parts of the processing through adaptive decision-making, and communicate results to workers and floor managers. The project builds on simulated manufacturing scenarios running on specialized equipment, such as the Fischertechnik Learning Factory, Automated Guided Vehicles (AGVs), Unmanned Aerial Vehicles (UAVs), and various sensor devices and cameras.
Your responsibility
- Independent and responsible research at the intersection of data stream processing systems, distributed systems, and edge-cloud architectures for smart manufacturing applications.
- Extending the NebulaStream framework to process heterogeneous, multimodal sensor and camera data streams (e.g., structured data, images, audio data, geolocation data).
- Developing stateful operators for the resource-efficient detection of complex event patterns across multiple data streams.
- Integrating resource-efficient AI models (edge AI) into the distributed streaming engine, while meeting strict latency, energy, and resource requirements.
- Developing declarative interfaces for creating action rules and for interacting with ongoing stream analyses and alerts at runtime.
- Validating the developed approaches using simulated manufacturing scenarios.
Your profile
- Successfully completed university degree (Master, Diplom, or equivalent) in computer science or a comparable field.
- Several years of experience in the areas of systems integration, distributed systems, and data stream processing is required.
- Very good programming skills in C++ or Rust are indispensable.
- Good German and/or English language skills are required; willingness to acquire any missing language skills
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Experience with edge/IoT systems, sensor fusion, complex event processing, the application of machine learning in streaming contexts, as well as experience in open-source projects is advantageous.
Employer: TU Berlin / BIFOLD
Salary Grade: E13, TVL Berliner Hochschulen
Starting Date: at the earliest possible (for 3 years)
Closing Date: October 23, 2026
Full job posting: IV-386/26