Below, you will find current topics that can be worked in the context of a Seminar Software Engineering, a Bachelor’s or a Master’s Thesis. The context indicates the scope of the work, and the keywords give you further information about the topic and its domain.
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Managing distressed patients in clinical environments is a challenging yet critical skill for healthcare professionals. Patients exhibit diverse emotional responses—ranging from anxiety and shyness to outright resistance-making it essential for clinicians to adapt their approach. Traditional training methods often lack the realism needed to prepare professionals for these high-stakes interactions.
Large language models (LLMs) have became popular over the last few years, one of the reason being the quality of the outputs these models generate. LLM Agents are a step further, they allow LLMs to use memory, tools and sequential thinking.
Merge conflict resolution is a critical challenge in software development, particularly in large, collaborative projects that use version control systems like Git. When multiple developers modify the same part of a codebase, conflicts arise that require manual intervention. Existing automated resolution strategies often rely on rule-based approaches or traditional machine learning models, which struggle with complex and ambiguous cases. Deep learning has the potential to improve conflict resolution by learning patterns from historical merge conflicts and predicting optimal resolution strategies. However, identifying the most effective deep learning architecture for this task remains an open question.
Large Language Models (LLMs) have shown strong performance in code generation, completion, and repair tasks. However, supervised pretraining on massive code corpora is limited by data quality, lack of explicit feedback, and the inability to capture correctness beyond next-token prediction. Recent research has explored Reinforcement Learning (RL) based training approaches to refine LLMs for code. By leveraging feedback signals—such as compilation success, test case execution, or static analysis warnings—models can be trained to better align with correctness and developer intent.
Modern public transportation vehicles, such as trams, buses, trolleybuses and trains, increasingly rely on on-board computing units to process and securely transfer large volumes of data generated by sensors and surveillance cameras. These systems often operate on limited battery power during night-time parking, when vehicles are disconnected from external energy sources. During this time window, the on-board computer must complete several computationally intensive tasks—such as software updates, video decoding, compression, encryption, and data upload—before service resumes.
In collaboration with Supercomputing Systems AG (SCS) and a public transportation company in Romandie, this project addresses the challenge of executing these tasks reliably under strict energy and time constraints. Understanding how to configure the embedded system and how to select optimal communication protocols for data transfer in order to remain both energy-efficient and predictable is essential for dependable fleet operations.