Hi, I'm Johannes 👋

I build robust scientific software and quantitative models for complex systems under uncertainty. My work combines mathematical modelling, Python, and scalable workflows to turn difficult research problems into practical and reproducible solutions.

I enjoy working at the intersection of physics, geophysics, natural hazard modelling, and data science, with a particular focus on uncertainty quantification, simulation, and FAIR scientific computing.

📫 Contact me at johanneskemper@gmx.ch

Projects & Codes

• specnm — Open-source Python package for computing gravito-elastic free oscillations (normal modes) of planets and moons, used in Earth, Mars, and Moon studies.

• DT-GEO Workflow Registry — FAIR digital twin workflows for geophysical extremes, enabling reproducible simulations across European HPC infrastructures.

• ds-ml-projects — A growing collection of machine learning and data science projects developed for learning, experimentation, and portfolio building.

Areas of Interest

• Quantitative modelling and uncertainty quantification
• Scientific software engineering and reproducible workflows
• High-performance computing (HPC)
• Natural hazard modelling
• Bayesian inference and simulation of complex systems

Connect

• 💼 LinkedIn: https://www.linkedin.com/in/johannes-kemper-9a7a83106
• 🦊 GitLab: https://gitlab.com/JohKem1