We are seeking motivated researchers (m/f/d) to explore learning-based methods that augment or replace classical components in modern stream processing engines, with a strong focus on practical systems research. * learning-based scheduling, parallelism, and resource allocation for streaming operators, and * Exploring the use of machine learning and, where appropriate, large language models (LLMs) to support system optimization, configuration, or semantic processing in streaming pipelines. * We welcome applications from candidates (m/f/x) with a systems background and an interest in learning-based approaches. * Interest in learning-based techniques for system optimization and performance modeling; prior ML experience is helpful but not mandatory.
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