Jannes Nys
Senior Researcher at ETH Zürich
Zurich, Zurich, Switzerland
Summary
👤
Senior🎓
Top SchoolJannes Nys is a senior researcher and computational quantum physicist with a decade of experience applying machine learning to many-body quantum systems, currently leading ML-driven research at ETH Zürich. His work bridges deep physics knowledge (PhD in Physics) and advanced AI training (Advanced Master's in AI, summa cum laude), blending theoretical insight with practical algorithm development. As a postdoc at EPFL and contributor to the prominent NetKet open-source library, he extended PauliStrings functionality and integrated OpenFermion conversions—efforts that improved interoperability for quantum ML tooling. He has a track record of translating early-stage research into impact, from co-founding an ML consultancy to building the core framework that helped BioStrand secure seed funding. Based in Zurich, he combines rigorous academic credentials with hands-on engineering to push computational techniques for fermionic and many-body problems.
10 years of coding experience
6 years of employment as a software developer
Advanced Master of Artificial Intelligence (MSc.), Engineering and Computer Science, Summa cum laude, Advanced Master of Artificial Intelligence (MSc.), Engineering and Computer Science, Summa cum laude at University of LeuvenDoctor of Philosophy - PhD, Physics, Doctor of Philosophy - PhD, Physics at Ghent University
Dutch, English, French
Github Skills (8)
quantum-computing10
machine-learning10
jax10
python10
numpy10
unit-testing9
deep-learning8
deeplearning-ai8
Programming languages (2)
Github contributions (5)
netket/netket
Jul 2021 - Jan 2023
Machine learning algorithms for many-body quantum systems
Role in this project:
ML Engineer
ML EngineerContributions:45 reviews, 20 commits, 28 PRs in 1 year 6 months
Contributions summary:Jannes contributed to the development of the `PauliStrings` operator, focusing on enhancements and extensions. They improved the operator's functionality by handling Hilbert spaces, matrix multiplication, and scalar multiplication. Furthermore, they integrated the `openfermion` library to convert `QubitOperator` objects into `PauliStrings`, significantly expanding the capabilities of the library. The user also added unit tests to ensure the correct behavior and integration of the changes.
markov-chain-monte-carlovariational-monte-carlomonte-carlo-methodsquantum-computingvariational-method
jwnys/netket
Jul 2021 - Dec 2024
Machine learning algorithms for many-body quantum systems
Contributions:146 pushes, 29 branches in 3 years 5 months
quantum-computingmachine-learning-algorithmsquantum-many-bodybodylearning-algorithms
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