AI Solutions Engineer — Clockworks
Since August 2026. Applied machine learning and computer vision, built into systems that clients actually use — the research-to-production gap as a day job. More detail here as projects ship.
The focused professional track: AI research, medical imaging, data work in public-sector contexts, teaching, and the hands-on infrastructure that ties it all together. Scroll for the full picture — or just email me.
Since August 2026. Applied machine learning and computer vision, built into systems that clients actually use — the research-to-production gap as a day job. More detail here as projects ship.
Master's thesis project on deep learning for dynamic MRI reconstruction. The core challenge: MRI acquisition is slow because of fundamental physics constraints — the goal is to learn how to reconstruct good-quality images from far fewer measurements, making scans faster without clinical compromise.
Worked on Data & AI projects in a public-sector environment where technical decisions have real consequences and non-technical stakeholders need to understand and trust them. A useful counterweight to pure research: the problem framing matters as much as the method.
Represented MSc AI students in the programme's study committee. Coordinated meetings between students and faculty, drafted agendas and minutes, and advocated for curriculum improvements based on student feedback. Kept a committee with competing views moving forward.
Designed and delivered physics lessons for HAVO and VWO students. Teaching is one of the best tests of understanding: if you can't explain it clearly to someone starting from scratch, you probably haven't fully understood it yourself. That principle still shapes how I write code, documentation, and presentations.
Built and maintain a personal Ubuntu server running Docker containers, nginx reverse proxy, Cloudflare tunnel and DNS, Minecraft hosting, and a GitHub Actions deploy pipeline. This site runs on it. Everything breaks eventually — which is how you actually learn networking, systemd, and when to just restart the container.
Graduate specialisation in machine learning and deep learning, with a thesis track connected to dynamic MRI reconstruction at Amsterdam UMC. Coursework covered advanced ML theory, computer vision, natural language processing, and AI ethics.
Erasmus exchange at one of Europe's top technical universities. Computer science coursework in an international setting — and enough time in Lisbon to pick up a B1 in Portuguese and a strong opinion about pastéis de nata.
Broad AI foundation: programming, discrete mathematics, linear algebra, probability theory, machine learning, cognitive systems, and the history and philosophy of AI. Where the engineering mindset was shaped.
Science-heavy VWO track with physics, mathematics, chemistry, and informatics. The place where both teaching and learning first felt like the same process.