I am a senior software engineer and AI specialist based in Basel, Switzerland, with a background in biotechnology and microscopy image analysis. Through my consultancy, I help pharma, biotech, and life-science R&D teams turn complex imaging and biological data into reproducible analysis pipelines, scientific software, and practical AI workflows.
My work sits at the interface of biology, data science, and engineering. I combine domain understanding with strong software and algorithmic expertise to help teams move from experimental complexity to robust, usable computational solutions.
Background
My academic training includes an MSc in Biotechnology and a PhD in Microscopy Image Analysis from ETH Zurich. I subsequently carried out postdoctoral research at The Scripps Research Institute and held long-term scientific software and image-analysis roles at the Friedrich Miescher Institute and at the Department of Biosystems Science and Engineering of ETH Zurich in Basel.
Over more than two decades, I have worked closely with experimental scientists, facility teams, and interdisciplinary collaborators in academia and industry-facing environments. This has given me a strong understanding of how to translate scientific questions into computational methods and how to build software that is rigorous, maintainable, and useful in practice.
What I Do
I support research and development teams with work such as:
- AI and computer-vision workflows for microscopy and biological imaging
- reproducible analysis pipelines for complex experimental data
- scientific software design and development
- integration of algorithms, data, and user-facing tools
- technical support for assay development, screening, and quantitative analysis
- custom solutions for teams working at the intersection of imaging, biology, and software
My focus is not on producing isolated prototypes, but on creating solutions that can be understood, maintained, and used effectively by real teams.
How I Work
I bring a combination of scientific depth and engineering discipline. In practice, that means:
- understanding the experimental context before choosing the technical solution
- designing workflows that are reproducible and transparent
- building software that balances technical sophistication with usability
- working closely with domain experts to ensure adoption and practical value
- aiming for solutions that remain robust as projects grow in complexity
I am particularly effective in settings where teams need someone who can bridge communication between researchers, data scientists, and software developers.
Selected Work
My work has included open-source and applied software projects in image analysis, quantitative biology, scientific computing, and instrument-facing R&D software. Selected examples include:
pyMINFLUX
A cross-platform tool for visualization and analysis of MINFLUX super-resolution microscopy data.
pyPOCQuant
A tool developed for automated quantification of point-of-care tests from images.
qute/quai
Work on deep-learning-based computer vision and reproducible AI workflows for microscopy image analysis, with subsequent commercial development. Get in touch for more details.
SpectraSorter
Software for droplet sorting based on real-time analysis of high-speed spectrophotometer measurements, combining quantitative signal analysis with software-hardware integration.
oBIT
A toolset for automated data and metadata registration from acquisition hardware into structured research data systems.
These projects reflect the type of work I enjoy most: technically demanding, scientifically grounded, and designed for real-world use.
Publications and Open-Source Work
My background includes peer-reviewed publications in microscopy, image analysis, quantitative biology, and related computational methods, as well as contributions to open-source software used by research communities.
Why Clients Work With Me
Clients and collaborators typically work with me when they need someone who can:
- understand complex scientific requirements quickly
- design technically sound solutions without unnecessary complexity
- build software that researchers can actually use
- connect algorithm development with practical deployment
- operate comfortably across science, engineering, and applied AI
Get in Touch
If you are working on a challenge involving imaging, biological data, scientific software, or AI for R&D, feel free to get in touch. I am always happy to discuss whether I can help directly or suggest a sensible next step.