Ariel Werle
Rotterdam, South Holland, Netherlands
Summary
👤
Senior🎓
Top SchoolAriel Werle is a data scientist with a PhD in physics and 10 years of experience translating astrophysics research into production-ready machine learning solutions. After developing spectral classification CNNs and Bayesian inference workflows for galaxy studies at INAF—contributing to 30+ publications—he now applies similar algorithmic rigor to non-destructive testing of oil and gas pipelines at Pipesurvey International. He has strong hands-on experience with end-to-end scientific pipelines, hyperspectral data processing, and deploying ML models in applied engineering contexts. Based in Rotterdam, Ariel blends deep theoretical training with practical engineering, comfortable moving models from research code to integrated field systems. An understated strength is his track record of turning complex, noisy scientific data into robust, interpretable models that inform both discovery and operational decision-making.
10 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Physics, Doctor of Philosophy - PhD, Physics at Universidade Federal de Santa Catarina
Github Skills (17)
spyder10
ide10
anaconda10
qt9
python-development9
language-server-protocol9
galaxy9
python8
spectral8
spectra8
starlight7
linear-regression7
synthesis7
python-modules6
statistics6
Programming languages (1)
Github contributions (5)
arielwrl/arielwrl.github.io
Jun 2020 - May 2025
Contributions:9 pushes, 1 branch in 4 years 11 months
arielwrl/starlight_toolkit
Jun 2017 - Jan 2021
A set of tools to manage the Starlight spectral synthesis code.
Contributions:72 commits, 2 PRs, 76 pushes in 3 years 7 months
spectralsynthesisstarlight
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