I like AI systems that help people read, understand, and organize scientific work. I am also interested in machine learning, scientific machine learning, control systems, and dynamical systems.
I hold an M.Sc. in Electrical Engineering from TU Hamburg. My thesis work focused on coupled port-Hamiltonian systems on graphs, Neural ODEs, and neural-network-based modeling of dynamical systems.
I currently work in a small B2B IT environment, where I handle IT operations, build internal Python and Microsoft Excel-based tools, automate business workflows, deploy internal systems, and improve operational processes with data.
Outside of work, I spend time on scientific ML, control and dynamical systems, and on building tools for research workflows.
JARVIS RD Assistant is a self-hosted research workspace for literature discovery, source-linked RAG, PDF workflows, Zotero integration, AI assisted flash card generation with spaced repetition scheduling, and project management.
I originally started JARVIS to improve my own research workflow. It supports local model inference through Ollama as well as optional cloud models through LiteLLM.
- Machine learning and scientific ML: neural differential equations, structure-informed learning, graph-based models, and learning dynamical systems from data.
- Dynamical systems: control theory, system identification, port-Hamiltonian systems, modelling, and simulation.
- Applied automation: IT operations, internal tools, business-process automation, data workflows, and practical digitization.
- Engineering domains: electrical and power systems, robotics and autonomy, medical technology, aviation and aerospace.
- AI research tooling: retrieval-augmented generation, evidence retrieval, and tools for working with scientific literature.
I am open to opportunities in ML/AI, scientific ML, research engineering, modelling, simulation, system identification, and control, particularly in Germany and Switzerland.