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Machine Learning and Optimal Control Intern
Alice & Bob. Advance optimal control strategies: Develop methods to improve the optimization of quantum gates and state-preparation protocols under realistic noise, dissipation, and hardware constraints.
Tech Stack
Tools & technologiesPythonPyTorch
About the role
Key responsibilities & impact- Advance optimal control strategies: Develop methods to improve the optimization of quantum gates and state-preparation protocols under realistic noise, dissipation, and hardware constraints.
- Explore RL-based control approaches: Formulate selected adaptive control or sequential experiment-design tasks as reinforcement learning problems, and evaluate when RL offers benefits over more structured physics-based methods.
- Design adaptive experiments: Build strategies that use measurement history and model uncertainty to choose the next most informative experiment or control setting.
- Study parameter sensitivity and identifiability: Leverage differentiable simulators and open-system models to understand which experiments best constrain key physical parameters.
- Support hardware integration: Account for practical constraints such as inference speed, transfer latency, and compilation time when designing methods for laboratory use.
- Cross-Functional Collaboration: Partner closely with physicists and ML researchers to interpret experimental data and translate complex physical requirements into robust software solutions.
Requirements
What you’ll need- Currently pursuing a Master’s degree in Physics, Machine Learning, Applied Mathematics, or a closely related field (seeking a 5-6 month internship).
- Strong academic background in physics, optimization, mathematical modeling, or control.
- Fluency in English (both written and spoken).
- Strong proficiency in Python programming.
- Familiarity with modern tensor libraries such as PyTorch or JAX, and/or quantum frameworks such as Qiskit or Dynamiqs.
- Experience working with open quantum systems, quantum optics, or superconducting circuits.
- Interest in optimal control, reinforcement learning, parameter estimation or adaptive experiment design.
- Experience training models or running large-scale simulations on GPU clusters is a plus.
- A proactive, curious mindset with a desire to test algorithms on real-world, noisy hardware rather than just relying on ideal simulations.
Benefits
Comp & perks- 1 day off per month
- Half of transportation cost coverage (as per French law)
- Meal vouchers with Swile, as well as access to a fully equipped and regularly stocked kitchen
ATS Keywords
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Hard Skills & Tools
optimizationmathematical modelingcontrolPython programmingreinforcement learningparameter estimationadaptive experiment designdifferentiable simulatorsquantum gatesstate-preparation protocols
Soft Skills
proactive mindsetcuriositycross-functional collaborationcommunication