Get involved
How to get involved
Are you interested in high-performance computing beyond our lectures and seminars? Do you want to work on exciting projects in the domain of parallel computing? There are multiple ways of getting involved in our research activities:
- Lab projects
- Theses
- Jobs
Lab projects
Every semester, we offer the Lab Course Parallel Programming Technology, where students solve challenging tasks in ongoing research projects related to one of our major research topics. The specific projects will be introduced during the kickoff session at the beginning of the semester.
Theses
We regularly offer bachelor's and master's thesis projects at the intersection of high-performance computing and the areas listed in the table below.
If you are interested, please email us with the following:
- Your topic(s) of interest (from the table below or a topic of your own)
- Your relevant background and qualifications
- Your CV
- Your transcripts of records from your bachelor's and, if applicable, master's program
We will then look for a suitable supervisor and get back to you, usually within two weeks.
The skills listed below are intended as guidance. Exact requirements depend on the individual thesis topic, and you do not need to meet all of the listed points. We use your interests, background, and previous coursework to identify a suitable topic and supervisor.
| Topic | Skills |
|---|---|
| Parallel programming tools | |
| Tools and methods for understanding, analyzing, and improving parallel programs. | Recommended: C/C++, Python, Linux, parallel programming Helpful: MPI, OpenMP, compilers/LLVM, performance analysis |
| Computational neuroscience | |
| High-performance computing combined with simulation and analysis methods for large-scale neuronal systems. | Recommended: Python, scientific computing, numerical methods, data analysis Helpful: C/C++, GPU computing, machine learning, neuroscience |
| Storage and resource management | |
| Efficient I/O, storage, scheduling, and resource management in HPC and related distributed environments. | Recommended: Linux, C/C++ or Python, computer systems, algorithms Helpful: Operating systems, distributed systems, I/O, Slurm/HPC systems |
| HPC for AI | |
| Efficient training and execution of AI models on parallel and heterogeneous computing systems. | Recommended: Python, machine learning, Linux, PyTorch or similar frameworks Helpful: CUDA/GPU programming, distributed training, C/C++, performance analysis |
| Algorithms and applications | |
| Design, implementation, and optimization of algorithms and scientific applications for parallel systems. | Recommended: Algorithms and data structures, C/C++ or Python, numerical computing Helpful: Parallel programming, GPU computing, scientific computing, domain knowledge |
Jobs
We currently have no vacancies.
