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À propos de moi
I build scientific software for lab-data automation and computational biology. My work turns experimental files and modeling assumptions into reproducible plots, reports, and configuration-driven pipelines.
I focus on three areas: Bayesian optimization and bioprocess modeling, protein structure and docking workflows, and reusable scientific data toolkits (chromatography analysis, report automation).
I prefer small, testable scripts over hidden state, and keep outputs close to the data they describe. Stack: Python, NumPy, pandas, SciPy, matplotlib, scikit-learn, PyTorch, Linux, GitHub Actions... Plus d’infos