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Summary: I'm a Computer Science and Physics student, currently writing up my Master thesis. I'm interested in Data & AI roles located in Zürich.

A few words about me

  • 🎓 My curiosity and interest to understand how things work led me to do a (Bachelor's and) Master's degree in Physics at the University of Stuttgart (Stuttgart, Germany). During these studies, I became very interested in artificial intelligence (AI), which led me to pursue a second Master's degree in Computer Science (with a major in Machine Learning, Data Science and Artificial Intelligence), which I' m currently completing at Aalto University (Helsinki, Finland).
  • 👨‍💻 I develop on BOSS, an open-source Bayesian machine learning library for optimization in computational materials science. For example here or here you can see parts of my code contributions.
  • 🔭 I research how multi-fidelity Bayesian optimization can accelerate the structure search in computational materials science. My recent work on this has received the best poster award at the Physics Days 2022, Finlands annual international Physics conference.
  • 🎓 I’m currently working on my second Master thesis in CS.
  • 🌱 I’m also learning about the fundamentals of web software development (including relevant languages and tools such as JavaScript, Docker / Docker Compose or PostgreSQL)
  • 👯 I’m looking to work in Zürich on a Data & AI role.
  • 📫 The best and fastest way to reach me is by sending me an email.

Manuel's Projects

dft_optimization_boss icon dft_optimization_boss

BOSS optimization routine, applied to a Gaussian process toymodel that fits potential energy values of Alanine, obtained through FHI-Aims.

multi-fidelity-bo icon multi-fidelity-bo

This repository contains data analysis scripts that I have used for my Master thesis research on multi-fidelity Bayesian optimization. The origin of the raw experimental data can be found in the thesis (not in this repository).

physics-days-2022 icon physics-days-2022

This repository contains research on multi-fidelity Bayesian optimization, that I have presented on the Physics Days 2022

tool-notes icon tool-notes

stuff that I find useful and want to collect here for re-usage

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