You can find a list of past published theses here: Theses.
If you are interested in a thesis with us, feel free to browse through this list. To get an overview over our groups' current topics, see below.
If not stated differently, the following offered theses below are intended for M.Sc. but can also be reduced in scope and handed out as B.Sc. theses. We are also always open for your ideas.
Lecturers are not always good in estimating the effort it takes to take their courses. This thesis should develop a tool for estimating the actual workload of a course. There are multiple methods with which this could be achieved (e.g. using AI to estimate task difficulty or using a survey like https://cat.wfu.edu/resources/workload2/). As part of the thesis, estimates should be callibrated against real courses (e.g. asking lecturers and students for their estimates).
Verschiedene Kurse im Studium können sich in Bezug auf den Aufwand stark unterscheiden. Da individuelle Unterschiede groß sein könnten und Selbsteinschätzungen sehr ungenau ausfallen, ist es gar nicht so leicht dazu verlässliche Daten zu bekommen. Im Rahmen dieser Arbeit soll ein Open Source Werkzeug entwickelt werden, dass objektive Workloaderfassung per Timetracking erleichtert. Dafür soll eine WebApp entwickelt und erprobt werden, die direkt auf Smartphones (und Laptops) von Studierenden läuft.
Students and especially computer science students are among both the early adopters of generative artificial intelligence and it's critics. Knowing which AI tools are used and how they are used, is important to improve learning and teaching. The goal of this bachelor thesis is to collect and evaluate data on this topic for the computer and data science courses.
AI is transforming education. AI chatbots are everywhere but more useful patterns only slowly emerge. In our CS Bachelor, we use the programming learning environment SmartBeans that provides students with tasks and automatic feedback based on unit testing. But this feedback is limited in scope and usefulness. The goal of this thesis is to improve the learning experience by adding state-of-the-art AI methods that go beyond chats, improving well-known factors in efficient learning e.g. cognitive load. The focus could either be on AI or on improving learning.
Serverless computing has emerged as a new execution model for cloud computing with many advantages such as improved resource sharing and utilization as well as reduced complexity for developers that under serverless only need to write function code without worrying about server infrastructure. These advantages are also interesting for other fields of computing, for instance, High-Performance Computing (HPC) as improving resource sharing on expensive compute clusters would be a great benefit and serverless could even work as a drop-in replacement for existing HPC function. In order to utilize serverless on-premise, one needs to choose a serverless platform to deploy from the various, avail- able open source platforms, however, with so many rapidly evolving open source serverless platforms, it has become difficult to draw comparisons between them. Furthermore, there are no standardized benchmarks for serverless platforms, therefore, in order to perform a systematic analysis, three workloads inspired by scientific computing were designed and implemented in this thesis to compare the performance and usability of three representa- tive open source Kubernetes-based serverless platforms. The workloads include an image processing task as well as an adaption of dgemm() for HPL and were developed and tested on a cluster of Virtual Machines (VMs) provided by the Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG)1. It was observed that the performance of these platforms can be adequate when compared to expected hardware limits, but overall there are still many opportunities for improvements in terms of performance and usability.
This thesis addresses the need for transparency in the energy (resource) consumption of AI services by proposing a standardized environmental sustainability labeling system. The goal is to analyze the energy consumption and computational load of different AI tasks (such as classification, generation, or scheduling) and translate them into simple, interpretable labels similar to those used in home appliances (e.g., A++ to E). The student will collect runtime and resource usage data for various AI models, evaluate their environmental impact, and propose a standardized method to display this information to users and developers.
Focusing on how context-sensitive AI models can improve the early detection of faults in high-performance computing (HPC) environments. The objective is to implement an adaptive mechanism that selects among pre-trained machine learning models based on system state, workload behavior, and error patterns. The research involves defining relevant system contexts, integrating multiple detection models, and evaluating their effectiveness in different runtime conditions using real or simulated log datasets. The outcome aims to increase fault detection reliability while maintaining scalability across heterogeneous HPC architectures.
This thesis focuses on designing a context-aware job scheduler powered by mutiple models to operate across heterogeneous computing environments—including cloud, edge, and HPC systems. The proposed system dynamically selects the most appropriate scheduling model based on job characteristics and system availability. The study will involve the development of an AI-based scheduler that adapts to varying resource types, historical job outcomes, and performance metrics. The research contributes to enhancing scheduling efficiency in multi-layered computing systems where task diversity and infrastructure constraints coexist.
This thesis aims to develop a minimal and efficient AI tool for identifying unusual behavior in log files generated by small-scale devices or sensors. The student will design a lightweight anomaly detection system optimized for environments with limited memory and compute capacity, such as embedded systems. Context indicators (e.g., temperature, timestamp frequency, error patterns) will be integrated to improve detection relevance. The resulting system will be suitable for real-world applications like monitoring edge devices or low-power IoT nodes.
This project involves the design and implementation of a web-based dashboard that visualizes the decision-making process and behavior of AI models configurable to use different available data sources and tool-sets in system maintenance tasks. The student will focus on presenting tool selection, confidence levels, warning predictions, and input variations over time in an interpretable and user-friendly format. The dashboard is intended to improve trust and transparency in predictive maintenance systems, particularly in dynamic environments where multiple data sources, models or strategies are deployed simultaneously.
Serverless Computing or Function-as-a-Service (FaaS) has emerged as a new paradigm for computing over the last few years. There exists a number of open source FaaS platforms based on Kubernetes as the container orchestration platform maps well to the components required for FaaS. However, most approaches to FaaS are still relatively naive and leave many performance improvements on the table. This work focuses on said limitations and aims to solve at least one of them and implement a proof of concept. Finally, the performance improvements should be benchmarked in a virtualized environment and on the HPC system.
SpiNNaker is a new kind of computer architecture, inititally designed to efficiently perform simulations of spiking neuron networks. It consists of a large number of low-powered ARM cores, connected with an efficient message passing network. This architecture together with the flexibility of the spiking neuron model make it also ideal for accelerating other types of algorithms such as optimization problems, constrain problems, live image and signal processing, AI/ML, cellular automata, finite element simulations, distributed partial differential equations, and embedded, robotics, and low powered applications in general. As part of the Future Technology Platform, the GWDG has acquired a number of SPiNNaker boards that will be available for the thesis. In this thesis, you will develop one (or more) applications for SPiNNaker, either with the high-Level Python or low-level C/C++ software stacks, characterize your solution, compare it to a pure CPU/GPU solution (or other hardware in the Future Technologa Platform), if possible apply it to a real case study, and study the power consumption of your program.
LIGGGHTS is a common code used for the simulation of macroscopic particles. It is based on the well-known molecular dynamics code LAMMPS. The variant used within the thesis is the academic fork LIGGGHTS-PFM which is under current development. Since LAMMPS already has some modules for GPU processing, it is the goal of the thesis to modify LIGGGHTS-PFM to make use of these capabilities. In a first step the best strategy for implementing LIGGGHTS-PFM on GPUs should be evaluated. Based on this a concept and initial steps of the implementation are expected. However, it is not required that all features of LIGGGHTS-PFM are implemented within the scope of the thesis. It is expected that the enhancement will improve the run-time performance and pave the road to particle simulations on GPUs. General programming experience is required. Knowledge in GPUcomputing and particle transport is beneficial but not mandatory.
Precice as already presented at the GöHPCoffee is a multiphysics framework which allows the combination of various simulation codes to perform coupled simulations. These can both include coupled thermal problems or topics related to fluid structure interaction. So far, there exists no possibility to perform a coupled particle simulation using preCICE since the only particle solver is not publicly available. It is the aim of this thesis to mitigate this limitation by implementing a precice-adapter for the particle solver LIGGGHTS-PFM. One possibility could be the modification of an existing OpenFOAM-adapter in preCICE. In addition, the thesis will compare the achievable performance with other coupling libraries using LIGGGHTS and its derivatives. General programming experience is required. Knowledge in simulation technology and particle transport especially in LIGGGHTS is beneficial but not mandatory.
The present thesis delves into the exciting research field of personalized teaching in High Performance Computing (HPC). The objective is to identify innovative methods and technologies that enable tailoring educational content in the field of high-performance computing to the individual needs of students. By examining adaptive learning platforms, machine learning, and personalized teaching strategies, the thesis will contribute to the efficient transfer of knowledge in HPC courses. The insights from this research aim not only to enhance teaching in high-performance computing but also to provide new perspectives for the advancement of personalized teaching approaches in other technology-intensive disciplines.
This thesis focuses on the compilation and analysis of training materials from various scientific institutions in the High Performance Computing (HPC) domain. The initial phase involves utilizing scraping techniques to gather diverse training resources from different sources. Subsequently, the study employs methods derived from Machine Learning and Statistics to conduct a comprehensive analysis of the collected materials. The research aims to provide insights into the existing landscape of HPC training materials, identify commonalities, and offer recommendations for optimizing content delivery in this crucial field.
This thesis delves into the realm of computer science education with a particular focus on High Performance Computing (HPC). Rather than implementing new tools, the research centers on the field of didactics, aiming to explore and assess various pedagogical concepts applied to existing HPC training materials. Leveraging Machine Learning tools, this study seeks to identify prevalent didactic approaches, analyze their effectiveness, and ascertain which strategies prove most promising. This work is tailored for those with an interest in computer science education, emphasizing the importance of refining instructional methods in the dynamic and evolving landscape of High Performance Computing.
This thesis focuses on the evolution of the certification processes within the High Performance Computing (HPC) domain, specifically addressing the adaptation and porting of an existing prototype from the HPC Certification Forum. The objective is to redefine, optimize and automate the certification procedures, emphasizing the validation of knowledge and skills in HPC. The study involves the redevelopment of the prototype to align with current industry standards and technological advancements. By undertaking this project, the research aims to contribute to the establishment of robust and up-to-date certification mechanisms and standards that effectively assess and endorse competencies in the dynamic field of High Performance Computing.
This project proposes an AI-driven supervision system that evaluates, filters, and regulates the input and output of AI services to ensure alignment with legal, ethical, and operational constraints. Leveraging a Retrieval-Augmented Generation (RAG) approach, the system dynamically retrieves applicable rules, standards, or compliance requirements and uses them to guide or constrain AI outputs. The framework can act as an evaluator, validator, or content filter, and is capable of binding model behavior to regional or domain-specific regulations through modular policy enforcement agents.
AgentFlow is a system designed to orchestrate multiple specialized AI agents for handling complex, multi-stage tasks across diverse data modalities. It enables structured coordination among agents such as generators, evaluators, planners, and tool-runners, allowing for seamless transitions between steps like data extraction, reasoning, transformation, and final output generation. The system supports hierarchical workflows and dynamic agent selection based on task needs, improving both automation and adaptability.
This research explores the next generation of Retrieval-Augmented Generation (RAG) systems with a focus on improving response quality, reducing latency, and optimizing both indexing and retrieval performance. It integrates advanced re-ranking, dynamic rewriting, and hybrid search techniques to better align the retrieved context with the user query. The system also adapts to domain-specific needs by incorporating fast, distributed retrieval pipelines and context-aware generation.
This topic investigates the design and implementation of a federated learning framework for fine-tuning large language models across distributed and privacy-preserving environments. The system supports collaborative learning without centralizing data, enabling secure and scalable model updates. Key challenges such as heterogeneity, communication overhead, and convergence efficiency are addressed through intelligent orchestration, model distillation, and adaptive optimization strategies.
This research explores a federated learning framework utilizing ONNX Runtime APIs to enable efficient, on-device training of AI models across heterogeneous platforms. The system addresses the diversity in hardware, operating systems, and model formats by leveraging the portability and interoperability of ONNX. It ensures privacy-preserving learning while managing challenges such as inconsistent compute resources, non-IID data distribution, and communication constraints. The goal is to build a flexible and scalable solution for federated training in real-world, multi-device AI ecosystems.
This research investigates a memory-efficient fine-tuning strategy for large language models (LLMs) by splitting the model into segments and training them sequentially. Only a subset of model segments is loaded into memory at any given time, enabling fine-tuning on resource-constrained devices. The project addresses key challenges including segment dependency management (parallel vs. sequential paths), efficient backpropagation across unloaded segments, and maintaining gradient consistency. This approach opens new possibilities for LLM training without requiring full-model memory allocation.
While the data analytics tool Apache Spark has already been available on GWDG systems for multiple years, Dask is an upcoming topic. Spark is primarily used with Scala (and supports Python as well), Dask on the other hand is a part of the Python ecosystem. The project proposal is to compare the deployment methods on an HPC system (via Slurm in our case), the monitoring possibilities and tooling available, and to develop, run and evaluate a concrete application example on both platforms.
Evolutionary algorithms are an established means for optimization tasks in a variety of fields. An existing code being used for molecular clusters using a now simpler target system shall be investigated in regards of e.g. another parallelization scheme, more efficient operators, better convergence behavior of optimization routines used therein, etc.
WebGPU is an emerging graphics API for modern GPU use in the browser. In this thesis the potential of it for AI inference directly in the browser is explored.
The defacto standard API for LLM inference tasks is the OpenAI API. However, it is not optimal in terms of bandwidth and other characteristics. For example, images for are base64 encoded in the protocol, wasting bandwidth and CPU cycles. The SSE protocol used for streaming also incurs overhead. Furthermore, the protocol's stateless nature requires the whole conversation to be sent on each request. This could be mitigated for example by exploiting modern Compression Dictionary Transport or HPACK/QPACK. In this work, such approaches are implemented and evaluated in comparison to the existing protocols for common AI inference tasks.
Especially in the noisy intermediate scale quantum computing era, hybrid quantum-classical approaches are among the most promising to achieve some early advantages over classical computing. For these approaches an integration with HPC systems is mandatory. The goal of this project is to design and implement a workflow allowing to run hybrid codes using our HPC systems and, as a first step, quantum computing simulators, extend this to cloud-available real quantum computers, and provide perspectives for future systems made available otherwise. Possible aspects of this work are Jupyter based user interfaces, containerization, scheduling, and costs of hybrid workloads. The final result should be a PoC covering at least some important aspects.
Neuromorphic computers, i.e., computers which design is inspired by the human brain, are mostly intended for machine learning. However, recent results show that they may prove advantageous for NP-complete optimization problems as well. In this area they compete with (future) Quantum Computers, especially with Quantum Annealing and Adiabatic approaches. The goal of this project is to explore the SpiNNaker systems avaialable at GWDG regarding their use in this type of problems. A successful project would encompass the implementation of a toy problem comparing it to implementations on other platforms.
The naive simulation of interacting condensed matter systems is an ocean-boiling problem because of the exponential growth of the Hilbert space dimension. This offers a great opportunity to apply many analytical approximations and advanced numerical methods in HPC.