Seven years making cells do things they weren't supposed to. The science was real. I'm ready for problems that move faster, and pay better.

My PhD was in physics, but the lab was cross-disciplinary from day one — physicists working with biologists, engineers working with clinicians, everyone translating for everyone else. I became good at that translation. And at something harder to name: the kind of problem-solving that looks like luck from the outside. Machines that wouldn't work for anyone else would work for me. Not magic — just patience, systematic thinking, and a refusal to accept that something broken couldn't be understood.

The work I'm most proud of isn't in any paper. It's a microscopy analysis macro with instance segmentation that reduced processing time by 80%. A custom double patch-clamp setup assembled from parts and stubbornness. Arduino code that ran overnight experiments so I didn't have to. Things that made the lab function better and made other people's work easier. I cared about that — probably more than I needed to.

I was still uncertain when I moved to California. The uncertainty resolved itself quickly. Working every weekend will do that. So will the slow realization that the system isn't broken — it's working exactly as designed, and it wasn't designed for people like me.

What the US gave me, unexpectedly, was scale. A different kind of distance from everything familiar forces a certain clarity. I understood what I was good at, what I wanted, and what I was no longer willing to trade away. At 34, not being able to afford a used car is not a temporary condition to endure — it's information.

The transition has been active, not passive. Mornings: job applications and case study practice. Afternoons: building. Three Python apps shipped solo, all integrating LLMs into real workflows — not to prove I can code, but because I find the problems genuinely interesting and because the fastest way to understand a technology is to use it for something you actually care about.

The competitive intelligence tool maps technical landscapes in five minutes. The fragrance recommender applies NLP to olfactory note pyramids. The gym tracker normalizes training load by bodyweight so the numbers mean something over time. All three started from curiosity. All three are on GitHub.

I visited most of the major art museums in Europe before I was thirty. I wrote my PhD thesis in LaTeX with a custom typography package because the default looked bad and that bothered me. I study fragrance note pyramids with the same attention I brought to experimental design. I care about fabrics, cities, the way a room is lit.

The same filter runs through everything else: I read fiction and essays and poetry and manga, but only if the writing is actually good. I watch films for the craft or the ideas, rarely for the spectacle. I play video games that are atmospheric and story-rich — small, precise indie things, not MMOs. I listen to metal for the technical density, to singer-songwriters for the opposite reason, and to most things in between depending on the day. I travel by train whenever possible, because the point is to arrive somewhere genuinely different and pay attention to it. I play football and volleyball and do kickboxing, mostly because moving well matters and because it keeps everything else in balance.

This isn't separate from the scientific work — it's the same sensibility. Precision. Attention. The conviction that how something is made matters, not just whether it functions. I bring that to everything I do, which is either a strength or an inconvenience depending on the context.