Quantum ML Intern: Neural States for Magnetic Systems

Responsibilities

• Develop and train Neural Quantum States (NQS + VMC), with pretraining of the NQS on QPU-generated datasets.
• 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 wave functions of magnetic Hamiltonians.
• Contribute to internal tools and publications.

Requirements

• Master 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, Py

  Torch, Tensor

  Flow).
• Experience working with high‑performance computing (HPC) environments.
• Ability to work collaboratively in a research team.
• Strong communication skills in English.

Certifications & Qualifications

• Master's degree
• PhD