Postdoctoral Fellow - Applied Machine Learning in Quantum Systems

Job description

Summary This role focuses on developing computational methods for in-the-loop stabilization of neutral atom Logical Quantum Processing Units (LQPUs). The position sits at the interface of quantum hardware and control systems, supporting both near-term experimental performance and the long-term development of control architectures for stable, fault-tolerant computing. The successful candidate will design and prototype state-of-the-art methods to enable reliable quantum operations and translate device measurements into actionable control feedback. Responsibilities include advancing capabilities such as measurement-informed feedback control and machine learning–driven inference. Key Responsibilities: - Develop and deploy machine learning models for high-fidelity quantum operation inference and control prediction. - Design and prototype in-the-loop control mechanisms that adapt sequences based on measurement outcomes and system state. - Collaborate with physics, quantum error-correction, hardware, and control teams to validate new stack components using experimental data and system-level performance Required Qualifications: - Ph.D. or equivalent experience in Physics, Computer Science,...