In the following you find currently available theses at the Bachelor's or Master's level. However, most of the topics are scalable and can be either expanded or narrowed down after consultation with the staff member.

If you are interested in one of the topics described below or would like to propose a related topic, please contact the respective person.

We strongly welcome individual modifications of the described topics and are open for additional suggestions. Please contact lehre ∂ dos.tu-berlin.de for any questions and suggestions.

Bachelor

• Predicting the Scaling Behavior of Spark Data Processing Workloads using Lightweight Profiling
• Adaptive Memory Management for Scientific Workflow Tasks
• Context-Aware Performance Modeling for Resource Management of Distributed Dataflows
• Utilizing Large Language Models (LLM) for Knowledge-Driven Scalable System Tuning
• Heterogenous Hardware Testbed of Federated Learning 
• Root cause analysis of errors to increase IT reliability

Master

• Continual learning for Open Set Recognition with Deep Generative Models
• Predicting the Scaling Behavior of Spark Data Processing Workloads using Lightweight Profiling
• Resource Management for Adaptive Memory Requirements of Scientific Workflows
• Context-Aware Performance Modeling for Resource Management of Distributed Dataflows
• Advancing Log Anomaly Detection and Root Cause Analysis for Enhanced IT Reliability in AI Operations (AIOps)
• Utilizing Large Language Models (LLM) for Knowledge-Driven Scalable System Tuning
• Ensuring Fairness and Addressing Sources of Bias in Federated Learning