Performance measurements, performance evaluations, and performance engineering play an important role when designing and implementing complex software systems and distributed architectures. If you are interested in this area and seek a potential topic, please contact me for further discussion and drafting.

Human psychology impacts users in their privacy behavior. This is relevant for understanding user behavior, but also when designing technical privacy solutions. If you are interested in this intersection of psychology and privacy and seek a potential topic, please contact me for further discussion and drafting.

Data-intensive systems manage and process large volumes of data. These systems come with inherent challenges in terms of scalability, parallelism, programming models, architectures etc. If you are interested in this area and seek a potential topic, please contact me for further discussion and drafting.

System performance evaluations produce quantities of structured data sets from measurements. These data sets need to be further analyzed to derive actual insights. The aim of this thesis is to evaluate how humans can be assisted in such analyses by (smaller) large language models (LLMs) that can run locally on commodity hardware. However, as LLMs are primarily good in processing and generating text-based content, this may require additional tooling to handle structured data and statistical analyses. As part of the thesis, different approaches and concrete solutions should be explored and compared. This thesis project is provided in collaboration with benchANT GmbH.

Increasingly capable AI models cannot only be used to vibe code projects without any programming skills, but also to assist engineers when enhancing, refactoring, and securing existing code. The aim of this thesis is to evaluate how well such models can be utilized for improving the performance of existing applications by identifying and mitigating performance issues. The thesis should particularly focus on publicly available coding models that can be deployed locally (e.g., gpt-oss:20b, Qwen3.5-coder:35b, glm-4.7-flash, gemma4:31b). As part of thesis, appropriate use cases for benchmarking the models need to be identified from literature and preparated. This involves different performance issues in software code - both obvious and rather subtle and difficult to detect. The models should then be tested and evaluated against these cases and finally synthesized in a comparative summary.

Security mechanisms in Vehicle-to-Everything (V2X) environments, such as misbehavior detection systems, generate outputs that indicate potential malicious behavior but do not directly provide a unified and interpretable trust representation. This thesis investigates methods for quantifying such outputs into subjective logic opinions that can be used by trust assessment frameworks. The focus lies on a machine learning–based approach that learns the mapping from security mechanism outputs to belief, disbelief, and uncertainty values. The proposed method will be compared against existing quantification techniques to evaluate improvements in accuracy, robustness, and interpretability. The evaluation will be conducted using realistic V2X datasets and scenarios.

GNSS spoofing poses a significant threat to systems relying on accurate positioning, particularly in safety-critical domains such as autonomous driving. However, the evaluation of spoofing detection methods is often limited by the lack of standardized benchmark datasets. This project addresses this gap by designing and implementing a benchmark dataset for GNSS spoofing detection systems. The dataset includes diverse and realistic spoofing scenarios with well-defined ground truth, enabling systematic and reproducible evaluation. The dataset is validated using existing GNSS spoofing detection approaches.

The growing connectivity of cooperative intelligent transportation systems (C-ITS) raises critical security concerns, particularly regarding the trustworthiness of exchanged information. Misbehavior Detection Systems (MDS) play a key role in identifying malicious or faulty behavior in vehicular networks. This thesis evaluates both AI-based and non-AI-based MDS approaches, comparing their detection performance and computational efficiency under different attack scenarios. Using a structured benchmarking framework and representative vehicular datasets, the study analyzes different kinds of MBDs.

Modern vehicles rely on complex in-vehicle networks to support safety-critical and comfort functions. As these networks become more interconnected, they face increasing security risks from malicious attacks and faulty components. Intrusion Detection Systems (IDS) are essential for detecting abnormal behavior and protecting the integrity of in-vehicle communication. This thesis evaluates different IDS approaches for automotive networks, including rule-based and machine learning methods. Using representative datasets and realistic attack scenarios, the study compares detection accuracy, false positive rates, and computational efficiency

This project will focus on improving the trustworthiness of generated information through the fine-tuning of the Llama 3 8b model using the Unsloth training performance optimization library. The primary goal is to enhance the reliability and accuracy of AI-generated content by leveraging advanced training techniques. The research will involve evaluating the performance of the Llama 3 8b model before and after fine-tuning, analyzing improvements in trustworthiness metrics, and developing new methodologies to further optimize the model’s performance.

This project will investigate the performance differences between Kolmogorov-Arnold Networks (KANs) and Multi-Layer Perceptrons (MLPs) in the context of image classification tasks. Kolmogorov-Arnold Networks offer a novel approach to neural network architecture based on mathematical foundations that differ from traditional MLPs. The primary goal of this research is to empirically compare these two types of neural networks to evaluate their classification accuracy. The outcome of this research may provide insights into the potential advantages of KANs over conventional MLPs in practical applications.

This project will focus on improving the trustworthiness of generated information through the fine-tuning of the Llama 3 8b model using the Unsloth training performance optimization library. The primary goal is to enhance the reliability and accuracy of AI-generated content by leveraging advanced training techniques. The research will involve evaluating the performance of the Llama 3 8b model before and after fine-tuning, analyzing improvements in trustworthiness metrics, and developing new methodologies to further optimize the model’s performance.

This project will investigate the performance differences between Kolmogorov-Arnold Networks (KANs) and Multi-Layer Perceptrons (MLPs) in the context of image classification tasks. Kolmogorov-Arnold Networks offer a novel approach to neural network architecture based on mathematical foundations that differ from traditional MLPs. The primary goal of this research is to empirically compare these two types of neural networks to evaluate their classification accuracy. The outcome of this research may provide insights into the potential advantages of KANs over conventional MLPs in practical applications.