As Quantum Control Scientist, you will work alongside theorists and experimentalists to develop and apply theoretical and computational methods for designing and optimizing quantum operations to advance research in optimal quantum control.

In this role, you will:

• Contribute to the development of theoretical and computational tools for optimal quantum control.
• Assist in the design and stabilization of quantum operations using Hamiltonian engineering and quantum error correction/mitigation.
• Apply and refine numerical optimization techniques, including GRAPE, Krotov, CRAB, and stochastic methods.
• Collaborate with experimental teams to implement and test control protocols.
• Explore machine‑learning‑based control strategies, such as reinforcement learning and Bayesian optimization.
• Contribute to patents, publications, and conference presentations.
• Stay engaged with the broader quantum control community through literature and scientific events.

Qualifications

• PhD in Physics or a related field, with a focus on quantum control, quantum computing, or quantum information science.
• Experience at least in one of the following topics: (i) optimal quantum control theory, focusing on the precise manipulation of quantum systems for high-fidelity gate design and robust quantum operations; (ii) in error mitigation and quantum error correction through control-based strategies that enhance performance and scalability; (iii) in advanced numerical optimization techniques, such as: (a) Linear and nonlinear programming for constrained optimization of control fields, (b) Gradient-based algorithms (e.g., GRAPE, Krotov, CRAB, automatic differentiation methods), (c) Stochastic and evolutionary algorithms for high-dimensional or noisy landscapes, or (d) Model-predictive and real-time feedback control strategies. (iv) in machine learning approaches to quantum control, including: (a) Reinforcement learning for adaptive pulse design and calibration and (b) Bayesian optimization and neural-network-assisted control for robust parameter tuning.
• Nice to have : exposure to Hamiltonian simulation or quantum many-body systems, familiarity with cold-atom platforms, and/or experience in integrating quantum algorithms with control strategies for near-term devices.
• Excellent problem-solving, communication, and teamwork skills.
• Curiosity, creativity, and a collaborative mindset.
• Passion for continuous learning and innovation.