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.

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.

State-machine replication is a concept to achieve fault tolerance. Each replica has be executed in a deterministic way. In order to allow concurrency so called deterministic multithreading approaches were developed, one of them is called the UDS scheduler. The task of this work is to extend the UDS implementation in order to inject application knowledge into the scheduling decisions so that more concurrency can be achieved compared to no knowledge. The knowledge is injected by calling methods in the scheduler by the application. Multiple different of such methods are to be implemented (e.g., last lock, next lock) and integrated into the scheduling algorithm. Further especially for a Bachelor's thesis some evaluations should be applied in order to show the benefit of the injected knowledge. The work will need some acquaintance with the theoretical basics of the UDS scheduler. The implementation needs skills in Java.

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.