Evert Van Nieuwenburg
Assistant Professor at Leiden University
Leiden, South Holland, Netherlands
Summary
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Senior🎓
Top SchoolEvert Van Nieuwenburg is an associate professor based in Leiden with nine years of experience at the intersection of machine learning and quantum physics. He develops ML methods for quantum control, error correction, and representation learning, and transitioned from assistant to associate professor after roles at Leiden, the Niels Bohr Institute, and Caltech. Evert contributes to prominent open-source tooling like NetKet, where he implemented supervised learning capabilities and tutorials for many-body quantum systems. He is also a creative educator and creator of “quantum games” that make abstract quantum concepts accessible and interactive. Trained with a PhD from ETH Zürich, he blends rigorous theoretical background with practical software engineering to push experimental and algorithmic frontiers. Colleagues note his knack for turning advanced research code into usable frameworks and pedagogical demos.
9 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD, Theoretical and Numerical Physics, Doctor of Philosophy - PhD, Theoretical and Numerical Physics at ETH ZurichMaster of Science (MSc), Theoretical Physics, Cum Laude, Master of Science (MSc), Theoretical Physics, Cum Laude at Universiteit Leiden
Dutch, English, German
Github Skills (12)
algorithm10
machine-learning10
machine-learning-algorithms10
jax10
python10
supervised-learning10
neural-network9
deep-learning9
simulation8
simulations8
simulator8
physics8
Programming languages (6)
Github contributions (5)
netket/netket
Dec 2018 - Jan 2019
Machine learning algorithms for many-body quantum systems
Role in this project:
ML Engineer
ML EngineerContributions:41 commits, 4 comments in 5 days
Contributions summary:Evert primarily contributed to the development of supervised learning capabilities within the NetKet framework. Their work involved implementing supervised learning code mirroring the existing unsupervised learning functionalities. This included creating Python bindings, developing a tutorial for supervised learning using an Ising model, and refactoring the code with a new loss function and output logging. The user also made adjustments to the core supervised learning module and its integration with the overall NetKet architecture.
markov-chain-monte-carlovariational-monte-carlomonte-carlo-methodsquantum-computingvariational-method
everthemore/opyrators
Jul 2019 - Mar 2021
Contributions:27 commits, 4 pushes, 3 branches in 1 year 7 months
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