Matteo Degiacomi is a computational biophysicist and Reader at the University of Edinburgh with 11 years of experience developing methods for molecular modelling, integrative structural biology, and mass spectrometry data analysis. He bridges computer science and biotechnology, applying machine learning and optimization to molecular simulation, protein assembly, and interpretive mass-spec problems. Previously he led a research group at Durham University and built open tools (e.g., the POW optimization environment) during roles at EPFL and Oxford, demonstrating a commitment to open-source method development. His work uniquely combines rigorous algorithm development with hands-on biophysical modeling, enabling insights into complex assemblies rather than just tool-building. Trained at EPFL with a PhD in Biotechnology and a Master’s in Computer Science, he frequently collaborates across informatics and chemistry boundaries. Colleagues describe him as a method-focused scientist who turns challenging experimental data into predictive models.
11 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Biotechnology, Doctor of Philosophy (Ph.D.), Biotechnology at Ecole polytechnique fédérale de Lausanne
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Matteo Degiacomi - Reader at The University of Edinburgh