Student Applications

I regularly supervise Master projects and occasionally hire HiWis for research tasks in machine learning for Earth observation, maritime data, point clouds, and spatio-temporal modelling.

Good Fit

You should have a solid foundation in machine learning. The best evidence is practical: completed ML projects, a GitHub/repository link, a thesis or seminar report, or strong coursework in machine learning/deep learning. Relevant lectures may include advanced ML or deep learning courses such as the Advanced Neural Networks course.

Useful skills:

• Python and common ML libraries such as PyTorch, TensorFlow, JAX, or scikit-learn.
• Experience with data analysis, model training, evaluation, and readable code.
• Interest in remote sensing, environmental data, maritime applications, time series, or point clouds.
• Ability to work independently and communicate progress clearly.

Possible Topic Ranges

• Machine learning for satellite imagery and environmental monitoring.
• Deep learning for LiDAR point clouds and biomass estimation.
• Spatio-temporal models for sensor data and Earth observation time series.
• Efficient ML, input selection, or learning under resource constraints.

HiWi/Research Assistant Applications

HiWi tasks usually involve coding, data preparation, experiments, literature work, reproducibility checks, or support for research prototypes. Please include:

• Short motivation: what you want to work on and why.
• CV or brief academic background.
• Transcript or list of relevant completed courses.
• Links to code, reports, projects, or other evidence of ML experience.
• Possible start date.

If your background matches the topic and a position is available, I will invite you for a short meeting to discuss the project, expectations, and mutual fit.

Master Projects

For Master projects, please send a concise email with:

• Your study program and expected project/thesis timeline.
• The ML topics or application domains you are interested in.
• Relevant courses, projects, and programming experience.
• A first idea, if you already have one. A rough direction is enough.

If your background and timeline fit, and supervision capacity is available, I will suggest a meeting to discuss possible topics more concretely.