Machine Learning Internship – Rydberg-Based Quantum Simulators

Jobtailor; Massy; Sur place; EUR 30 000 - 54 000; Plein temps

Résumé du poste

Jobtailor is seeking a Master’s or PhD student in quantum many-body physics to develop and train Neural Quantum States (NQS) and VMC, with pretraining on datasets generated by a QPU. You will join a collaborative research team in Massy, Île-de-France, working on state-of-the-art numerical experiments.

You will benchmark against exact diagonalization, standard VMC and tensor networks, and apply NQS to observables and wavefunctions of magnetic Hamiltonians.

Qualifications

• Master’s or PhD student in quantum many-body physics.
• Proficiency in Python or Julia.
• Experience applying ML to quantum many-body systems.
• Experience with numerical methods for quantum spin systems.
• Familiarity with JAX, PyTorch or TensorFlow.
• Experience working in HPC environments.
• Ability to work collaboratively in a research team.
• Strong communication skills in English.

Responsabilités

• Develop and train Neural Quantum States (NQS + VMC), with pretraining of the NQS on datasets generated by the QPU.
• Benchmark this approach against established numerical methods (e.g., exact diagonalization, standard VMC, tensor networks) and against raw QPU data.
• Apply NQS to represent observables and many-body wavefunctions of magnetic Hamiltonians.
• Contribute to internal tools and publications.

Connaissances

Python

Julia

Machine learning for quantum systems

HPC

Team collaboration

English communication

Formation

Master’s or PhD student in quantum many-body physics

Outils

JAX

PyTorch

TensorFlow

NumPy

Description du poste

Responsibilities

• Develop and train Neural Quantum States (NQS + VMC), with pretraining of the NQS on datasets generated by the QPU.
• Benchmark this approach against established numerical methods (e.g., exact diagonalization, standard VMC, tensor networks) and against raw QPU data.
• Apply NQS to represent observables and many-body wavefunctions of magnetic Hamiltonians.
• Contribute to internal tools and publications.

Qualifications

• Master’s or PhD student in quantum many-body physics.
• Proficiency in one or more programming languages such as Python or Julia.
• Demonstrated experience with machine learning methods applied to quantum many-body systems (e.g., neural quantum states, supervised and unsupervised ML, kernel methods).
• Experience with numerical methods for quantum spin systems (e.g., exact diagonalization and variational Monte Carlo).
• Familiarity with scientific computing frameworks (e.g., JAX, PyTorch, TensorFlow).
• Experience working in high-performance computing (HPC) environments.
• Ability to work collaboratively in a research team.
• Strong communication skills in English.