Background

Studies

I did a dual BSc in Mathematics and Computer Science at the University of Granada, studying them side by side. The combination gave me a solid foundation in probability, statistical inference, linear algebra, and algorithms, and it shaped how I think about problems: I want to understand why something works, not just that it does.

From there I got interested in quantum computing and moved to Copenhagen to do an MSc in Quantum Information Science at the University of Copenhagen. My thesis looked at how noise affects the thermal states produced by shallow quantum circuits, using tensor-network simulation in Python.

Engineering

While finishing the MSc, I spent over a year at the Novo Nordisk Quantum Computing Programme as a part-time student engineer. I built an internal knowledge platform for researchers together with one other developer, largely without close supervision. I wrote most of the codebase: a FastAPI backend, JWT-based auth with HTTP-only cookies, async APIs, and a Vue.js frontend. It taught me a lot about the difference between code that works once and code that keeps working.

Before that I did an applied AI internship at Avanade, where I trained and deployed a computer vision model to edge devices via Azure. It was a useful introduction to what deployment actually looks like outside a research setting.

What I am looking for

I want roles that need both quantitative reasoning and solid engineering, things like AI engineering, model validation, quantitative development, or data and AI consulting. What I find most interesting is the boundary between modeling and production software, where you have to get the math right and then make sure it stays right in a real system.

I am direct in how I communicate, skeptical of oversimplification, and try to be explicit about my assumptions. I prefer doing fewer things well over doing many things quickly.