Patrick Huembeli
Founding Research Engineer at Noumenal Labs
Zurich, Zurich, Switzerland
Summary
👤
Senior🎓
Top SchoolPatrick Huembeli is a research-driven engineer with nine years of experience applying advanced ML, probabilistic modeling, and quantum computing to real-world problems, now focused on deploying robotics at Noumenal. He has bridged academic depth (PhD-level work in quantum information and hybrid quantum-classical optimization) with industry impact, leading AI efforts at Axiomatic to accelerate photonic chip design and at Extropic to build a JAX-based energy-model platform for time series and sensor fusion. Comfortable across theory, libraries and cloud deployment, he has a track record of extracting informal mathematics from literature, integrating MCMC and hardware accelerators, and collaborating with teams from prototyping to fabrication. Patrick’s background as an electronics technician and philosopher-educated thinker gives him a rare practical-plus-conceptual lens that helps translate noisy, physical systems into robust ML solutions.
9 years of coding experience
4 years of employment as a software developer
PhD Quantum Information, PhD Quantum Information at ICFOPhilosophy, Philosophy at Kantonsschuhle WohlenElectronics Technician, Electronics Technician at Apprenticeship as electronics technician at Lernzentren BadenMaster of Science - MS Physics, Master of Science - MS Physics at University of Basel
German, Spanish, French, English
Github Skills (50)
energy10
diffraction9
qml9
thermodynamics9
qiskit8
quantum-computing8
optimization8
adaptation8
automatic-differentiation8
physics8
high-energy-physics8
pytorch7
quantum-chemistry7
quantum-algorithms7
machine-learning7
Programming languages (4)
Github contributions (5)
physicsofEBM/physicsofEBM.github.io
May 2021 - Oct 2021
The Physics of Energy Based Models
Contributions:56 commits, 21 pushes, 2 comments in 4 months
physicsthermodynamicshigh-energy-physicsdiffractionenergy
PatrickHuembeli/qml-rg
Feb 2017 - Oct 2017
Contributions:18 PRs, 21 pushes, 2 branches in 7 months
quantum-computingreading-groupmachine-learningquantumquantum-machine-learning
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